{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression with a Neural Network mindset\n",
    "\n",
    "Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize  cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning.\n",
    "\n",
    "**Instructions:**\n",
    "- Do not use loops (for/while) in your code, unless the instructions explicitly ask you to do so.\n",
    "\n",
    "**You will learn to:**\n",
    "- Build the general architecture of a learning algorithm, including:\n",
    "    - Initializing parameters\n",
    "    - Calculating the cost function and its gradient\n",
    "    - Using an optimization algorithm (gradient descent) \n",
    "- Gather all three functions above into a main model function, in the right order."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Packages ##\n",
    "\n",
    "First, let's run the cell below to import all the packages that you will need during this assignment. \n",
    "- [numpy](www.numpy.org) is the fundamental package for scientific computing with Python.\n",
    "- [h5py](http://www.h5py.org) is a common package to interact with a dataset that is stored on an H5 file.\n",
    "- [matplotlib](http://matplotlib.org) is a famous library to plot graphs in Python.\n",
    "- [PIL](http://www.pythonware.com/products/pil/) and [scipy](https://www.scipy.org/) are used here to test your model with your own picture at the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'numpy'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mh5py\u001b[39;00m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'numpy'"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py\n",
    "import scipy\n",
    "from PIL import Image\n",
    "from scipy import ndimage\n",
    "from lr_utils import load_dataset\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "source": [
    "## 2 - Overview of the Problem set ##\n",
    "\n",
    "**Problem Statement**: You are given a dataset (\"data.h5\") containing:\n",
    "    - a training set of m_train images labeled as cat (y=1) or non-cat (y=0)\n",
    "    - a test set of m_test images labeled as cat or non-cat\n",
    "    - each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB). Thus, each image is square (height = num_px) and (width = num_px).\n",
    "\n",
    "You will build a simple image-recognition algorithm that can correctly classify pictures as cat or non-cat.\n",
    "\n",
    "Let's get more familiar with the dataset. Load the data by running the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the data (cat/non-cat)\n",
    "train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We added \"_orig\" at the end of image datasets (train and test) because we are going to preprocess them. After preprocessing, we will end up with train_set_x and test_set_x (the labels train_set_y and test_set_y don't need any preprocessing).\n",
    "\n",
    "Each line of your train_set_x_orig and test_set_x_orig is an array representing an image. You can visualize an example by running the following code. Feel free also to change the `index` value and re-run to see other images. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = [1], it's a 'cat' picture.\n"
     ]
    },
    {
     "data": {
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8ovfi7z2lqRdXMjYtefaSEpiUG7b/MhFzmJToHWvezJk5cFGIGGRvc2ZAeij6\nH2dOEWdrRk/vfudyT9z0jS8iJRH5toj8UEReEpF/1P38ARF5XkReE5E/EpHCzfoKBAJvD4wi6tcB\nfCKl9H4AjwP4tIg8CeC3AfxOSukRAOsAnrp7wwwEAncSo+TOSwBuyLz57l8C8AkAf7v7+ZcA/EMA\nvzdCf7v/Pfcae+QNOKe/dnRxh80wYsr2ak3yYmu1vWirdeVtFec9f9vSITU93X//w6ZOMipivvXq\nD0zd2roGAXXItDczY8k2GttqihKfkqqpY+bxdxypSL6oc1cq2vEvLKq5jFWV5OaD00Rl3Stkpqji\nPd/bqgv0KZGnXcaN49R9GlRz6v3v65UvvWSFywaNS3JWXeDUWJzHquK8LTnzrycVYXgxfdCjuZ/c\nEOb5Hib1U0CQOHPhfgN4RtrcE5FsN1PuKoBvAHgDwEZSP9ALAO4ZdH4gEHh7YaSFn1Jqp5QeB3AK\nwIcAPLpXs73OFZGnReQFEXlhTASigUDgJtiXOS+ltAHgOQBPAlgUkRvy4ykAlwac80xK6YmU0hN3\n2ekuEAiMiJvq+CJyBEAzpbQhIlMAfh67G3vfBPArAL4M4LMAvnp7Q2GX3WEDGlY1uHLQj44nkMxm\ntSFz5wPA4pISQBRKqncXS1YHZ1LO5Tmrt9auaZRZWv+2qXv1LTUfrtV0HNNFq9Nyirn5GavTVsiD\nt0JeqX6/gl1xOx37PUuc4poj8Jx+niiMbbZkH6U5SmVdoX0HmbFkGymnJs1Mzs7je993ulde39FI\nxvUNy6u/uaXm1GrZmk+bddXlmUS0VHJpsrN7RyQCLuX1qJmrvcnuVvamvHswbVR51+Fe4xHF6lHs\n+CcAfElEstiVEP44pfR1EXkZwJdF5H8B8H0AXxzpioFA4MAxyq7+jwB8YI/Pz2BX3w8EAu8wjN1z\n74bZoV9SYW+6ESFWTGdeuT5RaKDJxBNIqFiXceLx4pKm1Lrv9Lt75eKUMy9RpFrRmagefEzPe/jD\n1uPvJ1/U6LRViiTbaVmTHepqSpx3nnD5Kb2lPDubVftd2AuPI9MAQIh3sE0mTO8lyOm7Z12K61ma\nk5//qHraPfDTT5h2v/+nF3rlaWe2/I8+/Fiv/NYlNe1lCjZasUNkG/WGJRVp0dyxplKpWnWBn4Op\naXvPOvQctFte3dHyiBwdfSbkQaf1+VoOu0C3zpOIDEL46gcCE4hY+IHABOIAU2jJwCO/A8/HWcp4\nmnHtMhREk3EeXB0S+ZhAIpO1ndTIo6tTsCIfB860yLtrtmh3iAt5DXKZnrekEa2Mnrf8rsdN3cOP\n6u7663+xphUZK77OFI/1ygs+fwhkAAAfW0lEQVTVVVNXpx3o3LSKxLWmVReEBMycIxxJpAbkKYgm\n5ywgLboxnp/wwfvUn+vv/r2/1iuffv+nTbvTj75Og7KibL74UK+8el3n5rojBLlG/IT1uhXhM+RS\nWKRnJ+8CkxKNv1V2c8Ueee5VaZz8hknig6tG3u83loE+sV+654+2qx9v/EBgAhELPxCYQMTCDwQm\nEAen43v9nD7wene+oHrm9Izq07m8NeuwF16xaOvYU4158JueU550PU+2WSViyzoRbNZq06ZdoaBm\nqUrD6lw5UhJfu7hg6n7pFz7YKz//2ne1f8er/3d/9cle+UTnvKn753/4/V75yo5+t1zW6fE03y2X\nhptNVHxf8k6PZ0r/lLF7KotHHuiVv/UdPW+tYz27f+av/Y1e+a3z1uvupRfP9spXrqkJ860L50y7\n8iYRn5QtaUmOzIzGHOlIRRpVMmm66Dz2Xmw1XeTeLcWfDEkRdyvd3cJ58cYPBCYQsfADgQnEAWTL\n3RVKMs4Wx95MxZIV06dm1GtrekrLpelZWHB2WFvTJtMcB2RUqtY0ZDOeWrGuRrJts6JmowKlxQKA\nw0s6rpXLF0xdmQgqqmVrLvzoR9RT7Tf/a52DtQ0rAv/n/5l6UK++/j1Td8+31Btw6y3yiitblaZc\n0+OaE4/XrmqW4FpN50ecXMuEFVKw6o5MH+6VVyoq9r/1TXut+878Za9cnF40dW9cuNIrX7igKs32\n1rppV8qzyc6a6SrEGZgo30HZ8SS2OAMxLFoUZHQnQstlgClut//RLuAD0u4KEUcgEPirhVj4gcAE\nIhZ+IDCBGK+OL9Ljpvf52rI5SnXs6qZKqjOzKy7r6gCQJ0LJtjPXZPg3rtThCoM2mfd2dqwe2CYC\nTBEiZ3Qc7VtbqhfPzFqTXbWsrri5qtXTvv+K6qOPvedneuWfe9LuZfzwFc3H9xf/zt7C7JK6uS4S\nn+TlazZPX5sizuouUu3SBSUL6bQ4Nbg153GegYZzCd4mks6O6BiZYx8Azp09q/3PWPfmSl3vRYm4\n/r0+Wy/rdyt50hKK5Fu5rPezXB6cM8FH+N2RlA8yoAzckklwvzq9R7zxA4EJRCz8QGACMVZRX0R6\nKapKU1Z8zTvCCgPynErEppAcBxxMWmjHm0aiOYtunnstFYiLPlnxtVVTkXhlVU1NuYL9LoePqHmv\nULTkEu0Opbi2DnO4cOFir1yj1E+vzM7ZhmRGa6V5U7WwTL/lBS2//MpLph2TlnScrFkn0xanDS85\nFaxChCM7LlXYxYtne+Xle9Scl8naPr73/J/3yo1k30MlUpMOEdf/wtJh025u+nivXHWmyTal+cpk\n9XH30YQdikL04aGtxt7Pjsftit/jRLzxA4EJRCz8QGACMXZRP9+los45oowMBa+0Hdcdu0sxMUQH\ntl2dqJTzeas6cAAPE3bMzFpRmYN52m0bHMOOfAXyEBOXTfU6eb7lXSBRhiwPkrdzsHVdd/zPnntT\nx1S11oVDC+rhVszZ3+5GRb3aThxV8bjjZFTmy/NejkUaV4mow1PTzsd0UdtV61ZvuXjujPaf0Tko\nlKxadH1dLRR1ZxmYpkCo5UNKbT7lUpYxccj2hvXq297WudugtGdtx2NYLA7O+Wq9TH2W573P6ZP6\nzfGdTzChnq9BxBEIBAYgFn4gMIGIhR8ITCDGq+NnMmTecnz2beJvb1vFqdXWYZZIPWq7FNEcLZZ3\n3Ou8N5AnvZiJPQCgQTr4yoqNrGuRjlu8rNFiPk32wqKal8R5u3HbrQ3rTXf9uuq765uqq9Z2XPTc\nmu4FTGftXB2d0+tdS0Q+0rL7IeyFNz1t52CO0nxzFFitYvcaOLWX405Bg3TolcvqCbhwxCZVXj6m\nnPv8/QGblnz9+kqvXHS5BCo7NI+OWH5xWb/LVkXnMV+0+ytCptu6zzNAl/NRpcx1b6zLnq8jcfn2\nzX6em3+/GPmN302V/X0R+Xr3+AEReV5EXhORPxKRwbsjgUDgbYX9iPq/AeAVOv5tAL+TUnoEwDqA\np+7kwAKBwN3DSKK+iJwC8DcB/K8A/jvZlTM+AeBvd5t8CcA/BPB7N+9tV8xpdQab7DhgB7ABFCwk\ntZvWhJQnE2HGmUy4D+bqy+esStAms5f08c1rHZscmYsfABZmVcRuOO+/Kcoi68XG82/9pFdeW1Ox\n99Dho6Zdu6NjbjiT5uqmXnvjrHoCik9/RarP9KzNYLu4oMctUsHabesxV6urh9/8ovUuzBNJSoUC\nYhYWbSDO4tH7aEzWyxHQuZubUy++urvvBfK+XF29aOraRMSxQ+Qb3oTJ99aL4kyc4SXsToe9Rak/\np8qyNXWQCXCcGPWN/7sA/gFUczkMYCOl3lN9AcA9e50YCATefrjpwheRXwKwmlL6Ln+8R9M9dyxE\n5GkReUFEXvCbdoFA4GAwiqj/EQC/LCK/CKAEYB67EsCiiOS6b/1TAC7tdXJK6RkAzwBAvlh450Qx\nBAJ/hXHThZ9S+gKALwCAiHwMwH+fUvo7IvIvAPwKgC8D+CyAr970aimhdYPoYshPgLOADTZdeH2L\nyp6Ig9RF5Mjd1qd+5i6zbiCZjE5Xkcg7m473vkJRfPNzlkCyShFtK1esPtpgfntjqrRjvOdejXY7\nfNjq3ZUtIvooaQThxrYl2+Ael5aPm7r5edWn2YV5umQJNdeuas66rbLd53jfuzVHQHlbzWjLR0+a\ndnMUaefdistEhMJ7EnwfAOD6Nfqeay6XIJkxN8glOi8+nbbOfcdHfQ6B0esTRzzahzND363j9xcO\nIKrvdhx4Pofdjb7Xsavzf/HODCkQCNxt7MuBJ6X0HIDnuuUzAD5054cUCATuNsbquZdSUjOYk244\nXZUXtRpkNsoO86JK7EVlzWh8HvPqdVyUYIEIQZjPHwCa9b251+t16+l1/Zp6mTVdeqrFJfUkazes\neNwmsZrTOM86c1uOzF6eGCJf1DEvLOucll/8vmk3Qxx2c04dWT52b69cLOk4Lp+3qavYc7LhUpG1\nyOQ2v6jfGe7eZklWPuzyEzSJz75GeQySM5GeOaNm0J0d6w3J6lOH5ruUt89OnVJj9ZvzBnvJcVvr\n4efamVzbgyP89qFl3BbCVz8QmEDEwg8EJhBjT6F1g7Si33NPj8X9HLVItGuSzO7png3ldd5+Nabl\nzpKoldw4UtLzPElHlTy/TAZbnw6MdqC3XWBLflpF8Zzj45smjrnD5K03u2C93djs0fG/3fQ9K0Rk\n4YXVakW96a5es9aF+x5Qq8HsvKoZzdYbpt1OWcXvxUWrLhxe1O9SmNY+co58JE9EH5UtO1fs/ba6\nqtbizfU10271itZl844CnKaHHwmfJZlFbPFzSpM3uhowurrA0r2h/LgjvN57I974gcAEIhZ+IDCB\niIUfCEwgxq/jd3Ukr1s3yfzmyTYLRY1GY778nEuhxXsDWWdPMdzxxK/uU2G3yWTnNWNO+1Vl77wl\np4PT72nFpWpq1M/qtZ3tpkimxNKU7i/ML9novDylhU5Od8zQnDAh6LFj1jvv4gU1zVU2rc5cI478\nFunC005/XpjXMeYKloykXiPTZ0bnoNW27bap3fraVVN3/aqaRZlsY2vLEnZ0aH8o49ziCjTmRDkN\nWn6Licp+j4nhdXXW+Yc74A2rNJsIe/bdd+zv+z6JOeKNHwhMIGLhBwITiPGK+imh1eVi6zhZKxmx\n2orALN4XyfxTcIQdLO14Mg8W75lHzgtgzPNWcJz484eP9crVHRVfK2UbANOo63HTZV5ll66FBRtg\nw+a8Bnm+ZVy6Jw4Gybjf7jqZD8vEI3/0mKVLWCd+u/vuf8jULRgiDr0XDz7ybtPuyqrmDzh30Xr1\nNSgNV41SUNWcl2OLboAP0tnZUt5BFvWnpyxHIJs7G1WrWuVFn7OdbfL4y7hsykOy2bJZzauG/ACl\nAeXdY6NMmDoOBmvT89fnxJeGmQT3Z/qLN34gMIGIhR8ITCBi4QcCE4gDi87zJjsmtvQprgtkKuIo\nPnGuspwPLeuir0pMxEnntRw3f4si5Eo+r15T9SjWVWvOZGf491367xbtbVR3rL47O6/6+elHHuuV\ny1s24qxIhBjVLafTskszaYkdZ96cm1UyzPe893FT9zDp8lfXVM8+dsTuSaxTnroVikgEgCUycZ5/\n62yvfOWS3QtgF2Y/31NTOnflHXKD3rZ5BpgjXxyv/jaZXevkZt1q+eePcwnauRI69nVM5MLkL6mP\nZk77z7p8h01Owz2E9NOo8V6l3+crPN74gcAEIhZ+IDCBGLvn3g1vNS/GZEh2yWQHi/pMUOE58Vhs\n92mQWxk1kzBPuh9HllJodVwfdTbbsWhYsNNYyHD0n+2DcwHUa5akQ4gjf/WCppluVq3n3jylya5u\nXbfXphC0I4dV3PakJZvrarLrI9FoqZjKEYnnnalsY1NVEDYBAsDcrIrwPAfNZs20y3ZUbRFnKitk\ndR7rVfUmZE5DAJgSNfFmnQxcLuv1WpyG23m6MTdGGz41Gx+YKvO88HPqn78mXbtRt3XejDnoWsOw\nX67+eOMHAhOIWPiBwARivLv6ULEp43aZs7TrXnA74VkSX9sU2FJwGXFLdF67L+MueUSRqJV3wSUs\n+mezdnqm6Hq1qtZlp2ZtOwoMubbmAkqonCvYICNOSbW+ppTRLjYGJdrF3tywATapRVYJUkFOnrSe\nexwk9fLLL5q6DbIUNInMo+xUkzNnXuuVeQceABo0/1vk5ZibsjyGS8uqxoiz9GxuqpqRo+djdtZ6\n7rFYXXbpzAbtmPd5542a7MWJ36323qpbfzAPl10nmb1leunz/ts7mOdWEG/8QGACEQs/EJhAxMIP\nBCYQYzfn3YDXgYTNed5zirz1WM3x3n95Mq3Aca/XmWCDbB85x6vP+qInZGTPL/bWq7m0zZhTnX+e\n0jsDQLNE+xBNO35OKmrzDNh2fG1PaJIlIg4TD+ZIS2bn1ST4/R9azv3rG+oZV6A5Xd+0HnOXLitJ\n55M/9xFTt0ltOa11y5mu+N5Wq9bUt7mtJrws7cXMT1sdn02OnbafUy3XiWPf7wHdMkbVtZlW3+0v\nyADmj36yzVFJP26OkRa+iJwFsA2gDaCVUnpCRA4B+CMApwGcBfCfppTWB/URCATePtiPqP/xlNLj\nKaUnusefB/BsSukRAM92jwOBwDsAtyPqfwbAx7rlL2E3p97nhp0gUDHHmzSYf86nv2LO/UJexbxO\ny4rYdfI4azqRr0oEFUXis/dcZWZUjhOvVFJzHtHeoda22XK3tvTaRW9ynNJr57OWLGRrS8XjHHnx\nSdMG82Q6KhI3nZrRIdF5fV0962bmtky7hx9+tFe+dOmCqVsjko4M2ZTYSw0AHn3ve3vlQy7j7g6p\nI8vHNSXX5qYVCnfI1Dc7a019Obr29paOv1r3Ho+kIrkxFqeIr5E8PRsutVmLVLxh2Wv7+fH3JscY\nxqM/KnwQmiEB8ba+btWoKsCob/wE4M9E5Lsi8nT3s2Mppcu7F0uXARwdeHYgEHhbYdQ3/kdSSpdE\n5CiAb4jIj0e9QPeH4und8i2MMBAI3HGM9MZPKV3q/l8F8BXspsdeEZETAND9vzrg3GdSSk90NwTv\nzKgDgcBt4aZvfBGZAZBJKW13y78A4H8G8DUAnwXwW93/Xx3pit3F78k2iuTyWSxZvZg17yaREaLt\nospIb+szxRF/+zS52HrXYTaPNRpWd+efSWMGdKahSln10R23TzA7p2QTeeeLy9ferum1my4ajQlB\np1wK7SxFL1Zp/OubVsdfJILKj370k6bu3FnNkff6GS3n83bfZJrSiNf9fBsdWn/w8zl7b4+eONUr\n+9x5jbZOeJsm3+cj4Ig/aQxLnc4pre19L5D7tO9/YPScgxi927/k2BQ36gvQmT4H7CcAQLrh9tse\n2MRgFFH/GICvdN/WOQD/PKX0b0TkOwD+WESeAnAOwK+OdslAIHDQuOnCTymdAfD+PT5fA/DJ/jMC\ngcDbHeNPoXVD1Hdi7tS0it9TjjddSBxvkwmv07LiJZto2s7Ul8sxV59+7j3fOIqv4KLnOBowZzj8\nrNjIpBftlhUbqxQ9lstbjrkSfe8O9Z+cSrNZ1j58ZCDnApinFFcVl677wopy4s+4yLoqqUVtMiWK\nWPPjdeLcyxWnTd32jl5vZVUjDeHmm9NmbznPwOsUeZinSMm8M9mVijoHnlTEmu3YZOw490iK9qQl\nbFbrE+A55RXnaxjClzfMXGiu6642yhZZGpG9I3z1A4EJRCz8QGACEQs/EJhAjFfHF+mRVOYcUSYz\n8Igz9U2Ri63hrJ+yemWOdO3Usfoo56JrGSJEqxNxaunUp2MNyu/niEOpWaFo9wny9D3rFRuNttNS\n99Vshk1gphmKzPOes/o587xz3jvvQ8HzuOHy2a1Sumqeg3LFkm3OUFTcTsUy31QqaoKskTnyyNGT\nph3nCEjbtv/jpzSn31Xi408uq9xORcefzdjJ4v0Xa6bzurBQO2dGo7nzufMGmeb6c+dxef8RfX4c\ng88JHT8QCAxALPxAYAIxdnPeDdHIe+6xLNR26Y0MHzqh48QsJs5sOVNfp67HTOAhTuUoldSktu1S\nY6GgasAspXvacimumMyj3fHmPI6ss9+LJTn2LGu4MebJfNXy5ivyGmSPv9lFG0NVvqoe1px6HLCi\nc53SfHvu9k5Hx7hK5kEAWLuqx236nhcvnDXt6jWt88Shs3NKFjJHnoY7W5bANE/3s1azagvLyyz2\npz6RfRjJBXv8uYi5ASmvvDg/sglviDjPdbfr/h5v/EBgAhELPxCYQIxd1L/hFcWpsACbDstLMcyX\nl6ctbqcsGBHNqwfcZ4dSbfk0WRnyEEvO+48z6bKolXOeZCxGNhtWFOeMqjmXNZV54Fg0nJ623nlF\n4+1mU2hNER9dlghM1lYvmXa8Q18sWk/JLHk58nxzoBMArFzRPr1q1WhQ6iqa45Ljy2tQny0XFLW2\nquoCW0o6ncFibl9aNbp2q0W8js7bknf8h4npfSI2Ne0My7VlvPpcFX/Au/IZ32zwOG4cj6oAxBs/\nEJhAxMIPBCYQsfADgQnEWHX8jGQw3c1v50ko2VOtX/9i8x6ZNHwKMqrz/VcpxTN7DXrCjkKGo+6s\nxlQl81g+q2PMu3Y5qmu4QWaNJ5kdf472F2ZmrF7PuHRRdd9cwe6VLC6qTlul6MKdbRv5xtwhtYLV\nrYsUOVmgfYjZGWv2g2i7esN7L+r8b9Mcl7dtlCDrt335FLI6r2tXV3rlRUoTDgANMtX6FOuJ9jlY\nR261fCrswaa4Qe0Aq2vz87Iv3nuTE4+vNfzag8YxCuKNHwhMIGLhBwITiDEH6QCdASJJk0SvbF9w\nDP8+EYeaS5NlQjCcHO3F9t45Xt6ma2ddeq1Mm01xKmKXnLmtwmQbOatKNCltVs6RkfgAkxvwBBVM\ntsCmyd0xax/MRV9zprJZUiWWlo+YuulpDZxpUzDP9KwlDhlmArtyRck3mtfV5Oi9/4w3nbtFGVKZ\nmsRBuLVl+QOnKW12pWz5CfnZadNc+edhFFPZXrDedKbG9a9l/8wlSXu260uTzaf5IXW/5qgaRrzx\nA4EJRCz8QGACEQs/EJhAjJ9ss6vTDXZo7I9oYxMY8+p7wsQimbaazoW0UNTIPZNK2efOS4PJKzqs\nW9OOQqlkCUHy5FKbKvabsnsvu8buXptILygysOlMT8Wifk9PTMpfbYu49HOOzaNA5CZZl0Kb+UFz\nBW3Xdq6y7GK848x0NSLi4HY+NTjr8fmCHSPrwky42nD5AvNNnQ9PzlIn1+Es3U9Pspoz92IwJ/4w\nmMfFe/aaR9re9w6T4XO0n99+ygwZxz7zZscbPxCYQMTCDwQmEGMV9VNSMdunKS4RGYR4cwd5Xwnl\np+7jPxeO8BtcVyDxNedMdixS5l1dk8VGStedcdF5BRL1C67OiKyOc5/H3CKZ3afyZhF4dt56sV1b\nUQ+3FhOauLmqVFUU91zsTE4yN6tmv3zBqhysSmxtWjKSMnkKmjwD7lpMupJzov6oXnE8j1MzLlfB\nzGG9FkdlNqzZLzXp2KkBmSFmun5z8I0xerdSMhe6Kv6exqTpGhrRP+PrbuSf33M4fRjpjS8iiyLy\nL0XkxyLyioj8nIgcEpFviMhr3f9LN+8pEAi8HTCqqP+/Afg3KaV3Yzed1isAPg/g2ZTSIwCe7R4H\nAoF3AEbJljsP4K8D+C8BIKXUANAQkc8A+Fi32ZcAPAfgc8N7S73giGzWXTrDorgNsBGSX5howYvi\nLMr5HWIWk4w1wAWGcPBG23nF8e50lXjpvLpQIN6+YtGKlNUaqTheZKXdahYVM1krXvL8NBzHHKfo\n4gu0Gva71Gj8ydFJd+h7TtN3qTvxeI14+3zwDXtOMr+iJ8pg3sFm3ap/vOPP8PTrQhyE2bnjpm5q\ndlnHRHParFnvv0ZZv0trx3L6GbWgb6s97VEaDnEi/ECv0j5CkMHD2C8F3yhv/AcBXAXwT0Xk+yLy\nf3TTZR9LKV3eHVC6DODosE4CgcDbB6Ms/ByADwL4vZTSBwCUsQ+xXkSeFpEXROSFQRshgUBgvBhl\n4V8AcCGl9Hz3+F9i94dgRUROAED3/+peJ6eUnkkpPZFSesLH2QcCgYPBTXX8lNIVETkvIu9KKf0E\nwCcBvNz9+yyA3+r+/+ooFxTZe/G3ydOu5upYJyokIspw+wTG+0qsHtgmMogsjcHvNWRJr9zesrz6\nbHbhqDVP6sBWuozzimsn0q2dUsgSEat9nlyCo/o2KFV1t9c9x+vNS6zj12s2cq9jPPJ0DnzacCYx\n8enATRQl7anIEO8zr9MyU4mJgss6otYpNSgV506Yupkl1fk53Vij5jwNt9XsVyucN3WNLSUVbVes\n2bLT2Tvnw1CV25uaB1TJkAhCv6EwclquLka14/+3AP5AdhOknwHwX2FXWvhjEXkKwDkAv7qvKwcC\ngQPDSAs/pfQDAE/sUfXJOzucQCAwDow9SKdnh+gzYTC5hA2wYVkoEQEGp8ICXOZS7zhFoj+TVSRn\nsmsQ97o35zEnHHsQtp2UW6BUXo2cNU0yr17LEYnwFgh76+VcDgLDHecCVkYFB0L1e0pqmdUPn67L\nekcOEzUHB54Y77whBBjs8YiCzRCcm5rrlYvT1o9sak5JRvJkmmw0bLtcST0gswUb6MNemtXMWVOX\ndjSzsPf4Y8iQ+WHPSdaEM0PIPLwJdmRbYq/vQCAwcYiFHwhMIGLhBwITiDFH5yV0ukqeV0mM26Iz\n+bHux9ag1PY6lX6dfNHqxU0im+y0SY/P2D6alC+v6fTnjGj/haLq7s7ahlaLxuvMhTZVs+dop3Z0\nnnddbZBrqyeoNPshw0w8gy1DI2O/JqS9wCYrcX4exrTFBCYlG4GXK6mOn/X6f0ndeQtF1d2zedsu\niZoqvck5Q7kWkpvwKpnz2mVy9e1jmuEbM9iRzQYC+vnoDGi4/3sRb/xAYAIRCz8QmEDInRDXRr6Y\nyFUAbwFYBnDtJs3vNt4OYwBiHB4xDov9juP+lNKRmzUa68LvXVTkhZTSXg5BEzWGGEeM46DGEaJ+\nIDCBiIUfCEwgDmrhP3NA12W8HcYAxDg8YhwWd2UcB6LjBwKBg0WI+oHABGKsC19EPi0iPxGR10Vk\nbKy8IvL7IrIqIi/SZ2OnBxeRe0Xkm12K8pdE5DcOYiwiUhKRb4vID7vj+Efdzx8Qkee74/ijLv/C\nXYeIZLt8jl8/qHGIyFkR+UsR+YGIvND97CCekbFQ2Y9t4ctuRot/AuBvAHgPgF8TkfeM6fL/DMCn\n3WcHQQ/eAvCbKaVHATwJ4Ne7czDusdQBfCKl9H4AjwP4tIg8CeC3AfxOdxzrAJ66y+O4gd/ALmX7\nDRzUOD6eUnqczGcH8YyMh8o+pTSWPwA/B+Df0vEXAHxhjNc/DeBFOv4JgBPd8gkAPxnXWGgMXwXw\nqYMcC4BpAN8D8LPYdRTJ7XW/7uL1T3Uf5k8A+Dp2vdAPYhxnASy7z8Z6XwDMA3gT3b23uzmOcYr6\n9wBgMrML3c8OCgdKDy4ipwF8AMDzBzGWrnj9A+ySpH4DwBsANlLqsYOM6/78LoB/APTSDx8+oHEk\nAH8mIt8Vkae7n437voyNyn6cC38v/sGJNCmIyCyAPwHw91NKWzdrfzeQUmqnlB7H7hv3QwAe3avZ\n3RyDiPwSgNWU0nf543GPo4uPpJQ+iF1V9NdF5K+P4Zoet0Vlvx+Mc+FfAHAvHZ8CcGlA23FgJHrw\nOw0RyWN30f9BSulfHeRYACCltIHdLEhPAlgU6cUej+P+fATAL4vIWQBfxq64/7sHMA6klC51/68C\n+Ap2fwzHfV9ui8p+Pxjnwv8OgEe6O7YFAH8LwNfGeH2Pr2GXFhzYBz347UB2SeS+COCVlNI/Pqix\niMgREVnslqcA/Dx2N5G+CeBXxjWOlNIXUkqnUkqnsfs8/D8ppb8z7nGIyIyIzN0oA/gFAC9izPcl\npXQFwHkReVf3oxtU9nd+HHd708RtUvwigFexq0/+j2O87h8CuAygid1f1aewq0s+C+C17v9DYxjH\nR7Ertv4IwA+6f7847rEA+GkA3++O40UA/1P38wcBfBvA6wD+BYDiGO/RxwB8/SDG0b3eD7t/L914\nNg/oGXkcwAvde/N/A1i6G+MIz71AYAIRnnuBwAQiFn4gMIGIhR8ITCBi4QcCE4hY+IHABCIWfiAw\ngYiFHwhMIGLhBwITiP8fjgdmcWXGD6AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2018a407400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example of a picture\n",
    "index = 25\n",
    "plt.imshow(train_set_x_orig[index])\n",
    "print (\"y = \" + str(train_set_y[:, index]) + \", it's a '\" + classes[np.squeeze(train_set_y[:, index])].decode(\"utf-8\") +  \"' picture.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many software bugs in deep learning come from having matrix/vector dimensions that don't fit. If you can keep your matrix/vector dimensions straight you will go a long way toward eliminating many bugs. \n",
    "\n",
    "**Exercise:** Find the values for:\n",
    "    - m_train (number of training examples)\n",
    "    - m_test (number of test examples)\n",
    "    - num_px (= height = width of a training image)\n",
    "Remember that `train_set_x_orig` is a numpy-array of shape (m_train, num_px, num_px, 3). For instance, you can access `m_train` by writing `train_set_x_orig.shape[0]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples: m_train = 209\n",
      "Number of testing examples: m_test = 50\n",
      "Height/Width of each image: num_px = 64\n",
      "Each image is of size: (64, 64, 3)\n",
      "train_set_x shape: (209, 64, 64, 3)\n",
      "train_set_y shape: (1, 209)\n",
      "test_set_x shape: (50, 64, 64, 3)\n",
      "test_set_y shape: (1, 50)\n"
     ]
    }
   ],
   "source": [
    "### START CODE HERE ### (≈ 3 lines of code)\n",
    "m_train = train_set_x_orig.shape[0]\n",
    "m_test = test_set_x_orig.shape[0]\n",
    "num_px = train_set_x_orig.shape[1]\n",
    "### END CODE HERE ###\n",
    "\n",
    "print (\"Number of training examples: m_train = \" + str(m_train))\n",
    "print (\"Number of testing examples: m_test = \" + str(m_test))\n",
    "print (\"Height/Width of each image: num_px = \" + str(num_px))\n",
    "print (\"Each image is of size: (\" + str(num_px) + \", \" + str(num_px) + \", 3)\")\n",
    "print (\"train_set_x shape: \" + str(train_set_x_orig.shape))\n",
    "print (\"train_set_y shape: \" + str(train_set_y.shape))\n",
    "print (\"test_set_x shape: \" + str(test_set_x_orig.shape))\n",
    "print (\"test_set_y shape: \" + str(test_set_y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output for m_train, m_test and num_px**: \n",
    "<table style=\"width:15%\">\n",
    "  <tr>\n",
    "    <td>**m_train**</td>\n",
    "    <td> 209 </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**m_test**</td>\n",
    "    <td> 50 </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**num_px**</td>\n",
    "    <td> 64 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For convenience, you should now reshape images of shape (num_px, num_px, 3) in a numpy-array of shape (num_px $*$ num_px $*$ 3, 1). After this, our training (and test) dataset is a numpy-array where each column represents a flattened image. There should be m_train (respectively m_test) columns.\n",
    "\n",
    "**Exercise:** Reshape the training and test data sets so that images of size (num_px, num_px, 3) are flattened into single vectors of shape (num\\_px $*$ num\\_px $*$ 3, 1).\n",
    "\n",
    "A trick when you want to flatten a matrix X of shape (a,b,c,d) to a matrix X_flatten of shape (b$*$c$*$d, a) is to use: \n",
    "```python\n",
    "X_flatten = X.reshape(X.shape[0], -1).T      # X.T is the transpose of X\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_set_x_flatten shape: (12288, 209)\n",
      "train_set_y shape: (1, 209)\n",
      "test_set_x_flatten shape: (12288, 50)\n",
      "test_set_y shape: (1, 50)\n",
      "sanity check after reshaping: [17 31 56 22 33]\n"
     ]
    }
   ],
   "source": [
    "# Reshape the training and test examples\n",
    "\n",
    "### START CODE HERE ### (≈ 2 lines of code)\n",
    "train_set_x_flatten = train_set_x_orig.reshape(m_train, -1).T\n",
    "test_set_x_flatten = test_set_x_orig.reshape(m_test, -1).T\n",
    "### END CODE HERE ###\n",
    "\n",
    "print (\"train_set_x_flatten shape: \" + str(train_set_x_flatten.shape))\n",
    "print (\"train_set_y shape: \" + str(train_set_y.shape))\n",
    "print (\"test_set_x_flatten shape: \" + str(test_set_x_flatten.shape))\n",
    "print (\"test_set_y shape: \" + str(test_set_y.shape))\n",
    "print (\"sanity check after reshaping: \" + str(train_set_x_flatten[0:5,0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:35%\">\n",
    "  <tr>\n",
    "    <td>**train_set_x_flatten shape**</td>\n",
    "    <td> (12288, 209)</td> \n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>**train_set_y shape**</td>\n",
    "    <td>(1, 209)</td> \n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>**test_set_x_flatten shape**</td>\n",
    "    <td>(12288, 50)</td> \n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>**test_set_y shape**</td>\n",
    "    <td>(1, 50)</td> \n",
    "  </tr>\n",
    "  <tr>\n",
    "  <td>**sanity check after reshaping**</td>\n",
    "  <td>[17 31 56 22 33]</td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To represent color images, the red, green and blue channels (RGB) must be specified for each pixel, and so the pixel value is actually a vector of three numbers ranging from 0 to 255.\n",
    "\n",
    "One common preprocessing step in machine learning is to center and standardize your dataset, meaning that you substract the mean of the whole numpy array from each example, and then divide each example by the standard deviation of the whole numpy array. But for picture datasets, it is simpler and more convenient and works almost as well to just divide every row of the dataset by 255 (the maximum value of a pixel channel).\n",
    "\n",
    "<!-- During the training of your model, you're going to multiply weights and add biases to some initial inputs in order to observe neuron activations. Then you backpropogate with the gradients to train the model. But, it is extremely important for each feature to have a similar range such that our gradients don't explode. You will see that more in detail later in the lectures. !--> \n",
    "\n",
    "Let's standardize our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set_x = train_set_x_flatten/255.\n",
    "test_set_x = test_set_x_flatten/255."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What you need to remember:**\n",
    "\n",
    "Common steps for pre-processing a new dataset are:\n",
    "- Figure out the dimensions and shapes of the problem (m_train, m_test, num_px, ...)\n",
    "- Reshape the datasets such that each example is now a vector of size (num_px \\* num_px \\* 3, 1)\n",
    "- \"Standardize\" the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - General Architecture of the learning algorithm ##\n",
    "\n",
    "It's time to design a simple algorithm to distinguish cat images from non-cat images.\n",
    "\n",
    "You will build a Logistic Regression, using a Neural Network mindset. The following Figure explains why **Logistic Regression is actually a very simple Neural Network!**\n",
    "\n",
    "<img src=\"images/LogReg_kiank.png\" style=\"width:650px;height:400px;\">\n",
    "\n",
    "**Mathematical expression of the algorithm**:\n",
    "\n",
    "For one example $x^{(i)}$:\n",
    "$$z^{(i)} = w^T x^{(i)} + b \\tag{1}$$\n",
    "$$\\hat{y}^{(i)} = a^{(i)} = sigmoid(z^{(i)})\\tag{2}$$ \n",
    "$$ \\mathcal{L}(a^{(i)}, y^{(i)}) =  - y^{(i)}  \\log(a^{(i)}) - (1-y^{(i)} )  \\log(1-a^{(i)})\\tag{3}$$\n",
    "\n",
    "The cost is then computed by summing over all training examples:\n",
    "$$ J = \\frac{1}{m} \\sum_{i=1}^m \\mathcal{L}(a^{(i)}, y^{(i)})\\tag{6}$$\n",
    "\n",
    "**Key steps**:\n",
    "In this exercise, you will carry out the following steps: \n",
    "    - Initialize the parameters of the model\n",
    "    - Learn the parameters for the model by minimizing the cost  \n",
    "    - Use the learned parameters to make predictions (on the test set)\n",
    "    - Analyse the results and conclude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Building the parts of our algorithm ## \n",
    "\n",
    "The main steps for building a Neural Network are:\n",
    "1. Define the model structure (such as number of input features) \n",
    "2. Initialize the model's parameters\n",
    "3. Loop:\n",
    "    - Calculate current loss (forward propagation)\n",
    "    - Calculate current gradient (backward propagation)\n",
    "    - Update parameters (gradient descent)\n",
    "\n",
    "You often build 1-3 separately and integrate them into one function we call `model()`.\n",
    "\n",
    "### 4.1 - Helper functions\n",
    "\n",
    "**Exercise**: Using your code from \"Python Basics\", implement `sigmoid()`. As you've seen in the figure above, you need to compute $sigmoid( w^T x + b) = \\frac{1}{1 + e^{-(w^T x + b)}}$ to make predictions. Use np.exp()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: sigmoid\n",
    "\n",
    "def sigmoid(z):\n",
    "    \"\"\"\n",
    "    Compute the sigmoid of z\n",
    "\n",
    "    Arguments:\n",
    "    z -- A scalar or numpy array of any size.\n",
    "\n",
    "    Return:\n",
    "    s -- sigmoid(z)\n",
    "    \"\"\"\n",
    "\n",
    "    ### START CODE HERE ### (≈ 1 line of code)\n",
    "    s = 1.0 / (1.0 + np.exp(-1.0 * z))\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sigmoid([0, 2]) = [0.5        0.88079708]\n"
     ]
    }
   ],
   "source": [
    "print (\"sigmoid([0, 2]) = \" + str(sigmoid(np.array([0,2]))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table>\n",
    "  <tr>\n",
    "    <td>**sigmoid([0, 2])**</td>\n",
    "    <td> [ 0.5         0.88079708]</td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 - Initializing parameters\n",
    "\n",
    "**Exercise:** Implement parameter initialization in the cell below. You have to initialize w as a vector of zeros. If you don't know what numpy function to use, look up np.zeros() in the Numpy library's documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_with_zeros\n",
    "\n",
    "def initialize_with_zeros(dim):\n",
    "    \"\"\"\n",
    "    This function creates a vector of zeros of shape (dim, 1) for w and initializes b to 0.\n",
    "    \n",
    "    Argument:\n",
    "    dim -- size of the w vector we want (or number of parameters in this case)\n",
    "    \n",
    "    Returns:\n",
    "    w -- initialized vector of shape (dim, 1)\n",
    "    b -- initialized scalar (corresponds to the bias)\n",
    "    \"\"\"\n",
    "    \n",
    "    ### START CODE HERE ### (≈ 1 line of code)\n",
    "    w = np.zeros((dim,1))\n",
    "    b = 0\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    assert(w.shape == (dim, 1))\n",
    "    assert(isinstance(b, float) or isinstance(b, int))\n",
    "    \n",
    "    return w, b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w = [[0.]\n",
      " [0.]]\n",
      "b = 0\n"
     ]
    }
   ],
   "source": [
    "dim = 2\n",
    "w, b = initialize_with_zeros(dim)\n",
    "print (\"w = \" + str(w))\n",
    "print (\"b = \" + str(b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "\n",
    "<table style=\"width:15%\">\n",
    "    <tr>\n",
    "        <td>  ** w **  </td>\n",
    "        <td> [[ 0.]\n",
    " [ 0.]] </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>  ** b **  </td>\n",
    "        <td> 0 </td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "For image inputs, w will be of shape (num_px $\\times$ num_px $\\times$ 3, 1)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 - Forward and Backward propagation\n",
    "\n",
    "Now that your parameters are initialized, you can do the \"forward\" and \"backward\" propagation steps for learning the parameters.\n",
    "\n",
    "**Exercise:** Implement a function `propagate()` that computes the cost function and its gradient.\n",
    "\n",
    "**Hints**:\n",
    "\n",
    "Forward Propagation:\n",
    "- You get X\n",
    "- You compute $A = \\sigma(w^T X + b) = (a^{(0)}, a^{(1)}, ..., a^{(m-1)}, a^{(m)})$\n",
    "- You calculate the cost function: $J = -\\frac{1}{m}\\sum_{i=1}^{m}y^{(i)}\\log(a^{(i)})+(1-y^{(i)})\\log(1-a^{(i)})$\n",
    "\n",
    "Here are the two formulas you will be using: \n",
    "\n",
    "$$ \\frac{\\partial J}{\\partial w} = \\frac{1}{m}X(A-Y)^T\\tag{7}$$\n",
    "$$ \\frac{\\partial J}{\\partial b} = \\frac{1}{m} \\sum_{i=1}^m (a^{(i)}-y^{(i)})\\tag{8}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: propagate\n",
    "\n",
    "def propagate(w, b, X, Y):\n",
    "    \"\"\"\n",
    "    Implement the cost function and its gradient for the propagation explained above\n",
    "\n",
    "    Arguments:\n",
    "    w -- weights, a numpy array of size (num_px * num_px * 3, 1)\n",
    "    b -- bias, a scalar\n",
    "    X -- data of size (num_px * num_px * 3, number of examples)\n",
    "    Y -- true \"label\" vector (containing 0 if non-cat, 1 if cat) of size (1, number of examples)\n",
    "\n",
    "    Return:\n",
    "    cost -- negative log-likelihood cost for logistic regression\n",
    "    dw -- gradient of the loss with respect to w, thus same shape as w\n",
    "    db -- gradient of the loss with respect to b, thus same shape as b\n",
    "    \n",
    "    Tips:\n",
    "    - Write your code step by step for the propagation. np.log(), np.dot()\n",
    "    \"\"\"\n",
    "    \n",
    "    m = X.shape[1]\n",
    "    \n",
    "    # FORWARD PROPAGATION (FROM X TO COST)\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    A = sigmoid(np.dot(w.T, X) + b)                                    # compute activation\n",
    "    cost = -(1.0 / m) * np.sum(Y * np.log(A) + (1 - Y) * np.log(1 - A))                                # compute cost\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # BACKWARD PROPAGATION (TO FIND GRAD)\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    dw = (1.0 / m) * np.dot(X, (A - Y).T)\n",
    "    db = (1.0 / m) * np.sum(A - Y)\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    assert(dw.shape == w.shape)\n",
    "    assert(db.dtype == float)\n",
    "    cost = np.squeeze(cost)\n",
    "    assert(cost.shape == ())\n",
    "    \n",
    "    grads = {\"dw\": dw,\n",
    "             \"db\": db}\n",
    "    \n",
    "    return grads, cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dw = [[0.99845601]\n",
      " [2.39507239]]\n",
      "db = 0.001455578136784208\n",
      "cost = 5.801545319394553\n"
     ]
    }
   ],
   "source": [
    "w, b, X, Y = np.array([[1.],[2.]]), 2., np.array([[1.,2.,-1.],[3.,4.,-3.2]]), np.array([[1,0,1]])\n",
    "grads, cost = propagate(w, b, X, Y)\n",
    "print (\"dw = \" + str(grads[\"dw\"]))\n",
    "print (\"db = \" + str(grads[\"db\"]))\n",
    "print (\"cost = \" + str(cost))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:50%\">\n",
    "    <tr>\n",
    "        <td>  ** dw **  </td>\n",
    "      <td> [[ 0.99845601]\n",
    "     [ 2.39507239]]</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>  ** db **  </td>\n",
    "        <td> 0.00145557813678 </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>  ** cost **  </td>\n",
    "        <td> 5.801545319394553 </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### d) Optimization\n",
    "- You have initialized your parameters.\n",
    "- You are also able to compute a cost function and its gradient.\n",
    "- Now, you want to update the parameters using gradient descent.\n",
    "\n",
    "**Exercise:** Write down the optimization function. The goal is to learn $w$ and $b$ by minimizing the cost function $J$. For a parameter $\\theta$, the update rule is $ \\theta = \\theta - \\alpha \\text{ } d\\theta$, where $\\alpha$ is the learning rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: optimize\n",
    "\n",
    "def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False):\n",
    "    \"\"\"\n",
    "    This function optimizes w and b by running a gradient descent algorithm\n",
    "    \n",
    "    Arguments:\n",
    "    w -- weights, a numpy array of size (num_px * num_px * 3, 1)\n",
    "    b -- bias, a scalar\n",
    "    X -- data of shape (num_px * num_px * 3, number of examples)\n",
    "    Y -- true \"label\" vector (containing 0 if non-cat, 1 if cat), of shape (1, number of examples)\n",
    "    num_iterations -- number of iterations of the optimization loop\n",
    "    learning_rate -- learning rate of the gradient descent update rule\n",
    "    print_cost -- True to print the loss every 100 steps\n",
    "    \n",
    "    Returns:\n",
    "    params -- dictionary containing the weights w and bias b\n",
    "    grads -- dictionary containing the gradients of the weights and bias with respect to the cost function\n",
    "    costs -- list of all the costs computed during the optimization, this will be used to plot the learning curve.\n",
    "    \n",
    "    Tips:\n",
    "    You basically need to write down two steps and iterate through them:\n",
    "        1) Calculate the cost and the gradient for the current parameters. Use propagate().\n",
    "        2) Update the parameters using gradient descent rule for w and b.\n",
    "    \"\"\"\n",
    "    \n",
    "    costs = []\n",
    "    \n",
    "    for i in range(num_iterations):\n",
    "        \n",
    "        \n",
    "        # Cost and gradient calculation (≈ 1-4 lines of code)\n",
    "        ### START CODE HERE ### \n",
    "        grads, cost = propagate(w, b, X, Y)\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Retrieve derivatives from grads\n",
    "        dw = grads[\"dw\"]\n",
    "        db = grads[\"db\"]\n",
    "        \n",
    "        # update rule (≈ 2 lines of code)\n",
    "        ### START CODE HERE ###\n",
    "        w = w - learning_rate * dw\n",
    "        b = b - learning_rate * db\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Record the costs\n",
    "        if i % 100 == 0:\n",
    "            costs.append(cost)\n",
    "        \n",
    "        # Print the cost every 100 training examples\n",
    "        if print_cost and i % 100 == 0:\n",
    "            print (\"Cost after iteration %i: %f\" %(i, cost))\n",
    "    \n",
    "    params = {\"w\": w,\n",
    "              \"b\": b}\n",
    "    \n",
    "    grads = {\"dw\": dw,\n",
    "             \"db\": db}\n",
    "    \n",
    "    return params, grads, costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w = [[0.19033591]\n",
      " [0.12259159]]\n",
      "b = 1.9253598300845747\n",
      "dw = [[0.67752042]\n",
      " [1.41625495]]\n",
      "db = 0.21919450454067657\n"
     ]
    }
   ],
   "source": [
    "params, grads, costs = optimize(w, b, X, Y, num_iterations= 100, learning_rate = 0.009, print_cost = False)\n",
    "\n",
    "print (\"w = \" + str(params[\"w\"]))\n",
    "print (\"b = \" + str(params[\"b\"]))\n",
    "print (\"dw = \" + str(grads[\"dw\"]))\n",
    "print (\"db = \" + str(grads[\"db\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:40%\">\n",
    "    <tr>\n",
    "       <td> **w** </td>\n",
    "       <td>[[ 0.19033591]\n",
    " [ 0.12259159]] </td>\n",
    "    </tr>\n",
    "    \n",
    "    <tr>\n",
    "       <td> **b** </td>\n",
    "       <td> 1.92535983008 </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "       <td> **dw** </td>\n",
    "       <td> [[ 0.67752042]\n",
    " [ 1.41625495]] </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "       <td> **db** </td>\n",
    "       <td> 0.219194504541 </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise:** The previous function will output the learned w and b. We are able to use w and b to predict the labels for a dataset X. Implement the `predict()` function. There is two steps to computing predictions:\n",
    "\n",
    "1. Calculate $\\hat{Y} = A = \\sigma(w^T X + b)$\n",
    "\n",
    "2. Convert the entries of a into 0 (if activation <= 0.5) or 1 (if activation > 0.5), stores the predictions in a vector `Y_prediction`. If you wish, you can use an `if`/`else` statement in a `for` loop (though there is also a way to vectorize this). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: predict\n",
    "\n",
    "def predict(w, b, X):\n",
    "    '''\n",
    "    Predict whether the label is 0 or 1 using learned logistic regression parameters (w, b)\n",
    "    \n",
    "    Arguments:\n",
    "    w -- weights, a numpy array of size (num_px * num_px * 3, 1)\n",
    "    b -- bias, a scalar\n",
    "    X -- data of size (num_px * num_px * 3, number of examples)\n",
    "    \n",
    "    Returns:\n",
    "    Y_prediction -- a numpy array (vector) containing all predictions (0/1) for the examples in X\n",
    "    '''\n",
    "    \n",
    "    m = X.shape[1]\n",
    "    Y_prediction = np.zeros((1,m))\n",
    "    w = w.reshape(X.shape[0], 1)\n",
    "    \n",
    "    # Compute vector \"A\" predicting the probabilities of a cat being present in the picture\n",
    "    ### START CODE HERE ### (≈ 1 line of code)\n",
    "    A = sigmoid(np.dot(w.T, X) + b)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    for i in range(A.shape[1]):\n",
    "        \n",
    "        # Convert probabilities A[0,i] to actual predictions p[0,i]\n",
    "        ### START CODE HERE ### (≈ 4 lines of code)\n",
    "        if A[0, i] > 0.5:    \n",
    "            Y_prediction[0, i] = 1\n",
    "        else:\n",
    "            Y_prediction[0, i] = 0\n",
    "        ### END CODE HERE ###\n",
    "    \n",
    "    assert(Y_prediction.shape == (1, m))\n",
    "    \n",
    "    return Y_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions = [[1. 1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "w = np.array([[0.1124579],[0.23106775]])\n",
    "b = -0.3\n",
    "X = np.array([[1.,-1.1,-3.2],[1.2,2.,0.1]])\n",
    "print (\"predictions = \" + str(predict(w, b, X)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:30%\">\n",
    "    <tr>\n",
    "         <td>\n",
    "             **predictions**\n",
    "         </td>\n",
    "          <td>\n",
    "            [[ 1.  1.  0.]]\n",
    "         </td>  \n",
    "   </tr>\n",
    "\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "source": [
    "<font color='blue'>\n",
    "**What to remember:**\n",
    "You've implemented several functions that:\n",
    "- Initialize (w,b)\n",
    "- Optimize the loss iteratively to learn parameters (w,b):\n",
    "    - computing the cost and its gradient \n",
    "    - updating the parameters using gradient descent\n",
    "- Use the learned (w,b) to predict the labels for a given set of examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 - Merge all functions into a model ##\n",
    "\n",
    "You will now see how the overall model is structured by putting together all the building blocks (functions implemented in the previous parts) together, in the right order.\n",
    "\n",
    "**Exercise:** Implement the model function. Use the following notation:\n",
    "    - Y_prediction for your predictions on the test set\n",
    "    - Y_prediction_train for your predictions on the train set\n",
    "    - w, costs, grads for the outputs of optimize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: model\n",
    "\n",
    "def model(X_train, Y_train, X_test, Y_test, num_iterations = 2000, learning_rate = 0.5, print_cost = False):\n",
    "    \"\"\"\n",
    "    Builds the logistic regression model by calling the function you've implemented previously\n",
    "    \n",
    "    Arguments:\n",
    "    X_train -- training set represented by a numpy array of shape (num_px * num_px * 3, m_train)\n",
    "    Y_train -- training labels represented by a numpy array (vector) of shape (1, m_train)\n",
    "    X_test -- test set represented by a numpy array of shape (num_px * num_px * 3, m_test)\n",
    "    Y_test -- test labels represented by a numpy array (vector) of shape (1, m_test)\n",
    "    num_iterations -- hyperparameter representing the number of iterations to optimize the parameters\n",
    "    learning_rate -- hyperparameter representing the learning rate used in the update rule of optimize()\n",
    "    print_cost -- Set to true to print the cost every 100 iterations\n",
    "    \n",
    "    Returns:\n",
    "    d -- dictionary containing information about the model.\n",
    "    \"\"\"\n",
    "    \n",
    "    ### START CODE HERE ###\n",
    "    \n",
    "    # initialize parameters with zeros (≈ 1 line of code)\n",
    "    w, b = initialize_with_zeros(X_train.shape[0])\n",
    "\n",
    "    # Gradient descent (≈ 1 line of code)\n",
    "    parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)\n",
    "\n",
    "    \n",
    "    # Retrieve parameters w and b from dictionary \"parameters\"\n",
    "    w = parameters[\"w\"]\n",
    "    b = parameters[\"b\"]\n",
    "    \n",
    "    # Predict test/train set examples (≈ 2 lines of code)\n",
    "    Y_prediction_test = predict(w, b, X_test)\n",
    "    Y_prediction_train = predict(w, b, X_train)\n",
    "\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    # Print train/test Errors\n",
    "    print(\"train accuracy: {} %\".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))\n",
    "    print(\"test accuracy: {} %\".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))\n",
    "\n",
    "    \n",
    "    d = {\"costs\": costs,\n",
    "         \"Y_prediction_test\": Y_prediction_test, \n",
    "         \"Y_prediction_train\" : Y_prediction_train, \n",
    "         \"w\" : w, \n",
    "         \"b\" : b,\n",
    "         \"learning_rate\" : learning_rate,\n",
    "         \"num_iterations\": num_iterations}\n",
    "    \n",
    "    return d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following cell to train your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.693147\n",
      "Cost after iteration 100: 0.584508\n",
      "Cost after iteration 200: 0.466949\n",
      "Cost after iteration 300: 0.376007\n",
      "Cost after iteration 400: 0.331463\n",
      "Cost after iteration 500: 0.303273\n",
      "Cost after iteration 600: 0.279880\n",
      "Cost after iteration 700: 0.260042\n",
      "Cost after iteration 800: 0.242941\n",
      "Cost after iteration 900: 0.228004\n",
      "Cost after iteration 1000: 0.214820\n",
      "Cost after iteration 1100: 0.203078\n",
      "Cost after iteration 1200: 0.192544\n",
      "Cost after iteration 1300: 0.183033\n",
      "Cost after iteration 1400: 0.174399\n",
      "Cost after iteration 1500: 0.166521\n",
      "Cost after iteration 1600: 0.159305\n",
      "Cost after iteration 1700: 0.152667\n",
      "Cost after iteration 1800: 0.146542\n",
      "Cost after iteration 1900: 0.140872\n",
      "train accuracy: 99.04306220095694 %\n",
      "test accuracy: 70.0 %\n"
     ]
    }
   ],
   "source": [
    "d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:40%\"> \n",
    "\n",
    "    <tr>\n",
    "        <td> **Cost after iteration 0 **  </td> \n",
    "        <td> 0.693147 </td>\n",
    "    </tr>\n",
    "      <tr>\n",
    "        <td> <center> $\\vdots$ </center> </td> \n",
    "        <td> <center> $\\vdots$ </center> </td> \n",
    "    </tr>  \n",
    "    <tr>\n",
    "        <td> **Train Accuracy**  </td> \n",
    "        <td> 99.04306220095694 % </td>\n",
    "    </tr>\n",
    "\n",
    "    <tr>\n",
    "        <td>**Test Accuracy** </td> \n",
    "        <td> 70.0 % </td>\n",
    "    </tr>\n",
    "</table> \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "source": [
    "**Comment**: Training accuracy is close to 100%. This is a good sanity check: your model is working and has high enough capacity to fit the training data. Test error is 68%. It is actually not bad for this simple model, given the small dataset we used and that logistic regression is a linear classifier. But no worries, you'll build an even better classifier next week!\n",
    "\n",
    "Also, you see that the model is clearly overfitting the training data. Later in this specialization you will learn how to reduce overfitting, for example by using regularization. Using the code below (and changing the `index` variable) you can look at predictions on pictures of the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 1, you predicted that it is a \"cat\" picture.\n",
      "1.0\n",
      "b'cat'\n"
     ]
    },
    {
     "data": {
      "image/png": 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OTqLMudazLkX3rE1MJ7UxdAhyYfXSXqYOdjJ13Adn/rUqB7WsGE/PMgU/VWjd6ibwJkPX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\nOxx9CN/4Dkcf4v8DJDXNH7zGKX4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2018a61ba20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example of a picture that was wrongly classified.\n",
    "index = 1\n",
    "plt.imshow(test_set_x[:,index].reshape((num_px, num_px, 3)))\n",
    "print (\"y = \" + str(test_set_y[0,index]) + \", you predicted that it is a \\\"\" + classes[int(d[\"Y_prediction_test\"][0,index])].decode(\"utf-8\") +  \"\\\" picture.\")\n",
    "print(d[\"Y_prediction_test\"][0, index])\n",
    "print(classes[int(d[\"Y_prediction_test\"][0, index])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also plot the cost function and the gradients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sOwLAuEE5XBL2IsaX9tOFgiThFAoicebufLDjIK+t2sHsVTtZuHEvDQ4FfdKZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2018a66c908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot learning curve (with costs)\n",
    "costs = np.squeeze(d['costs'])\n",
    "plt.plot(costs)\n",
    "plt.ylabel('cost')\n",
    "plt.xlabel('iterations (per hundreds)')\n",
    "plt.title(\"Learning rate =\" + str(d[\"learning_rate\"]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "source": [
    "**Interpretation**:\n",
    "You can see the cost decreasing. It shows that the parameters are being learned. However, you see that you could train the model even more on the training set. Try to increase the number of iterations in the cell above and rerun the cells. You might see that the training set accuracy goes up, but the test set accuracy goes down. This is called overfitting. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6 - Further analysis (optional/ungraded exercise) ##\n",
    "\n",
    "Congratulations on building your first image classification model. Let's analyze it further, and examine possible choices for the learning rate $\\alpha$. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Choice of learning rate ####\n",
    "\n",
    "**Reminder**:\n",
    "In order for Gradient Descent to work you must choose the learning rate wisely. The learning rate $\\alpha$  determines how rapidly we update the parameters. If the learning rate is too large we may \"overshoot\" the optimal value. Similarly, if it is too small we will need too many iterations to converge to the best values. That's why it is crucial to use a well-tuned learning rate.\n",
    "\n",
    "Let's compare the learning curve of our model with several choices of learning rates. Run the cell below. This should take about 1 minute. Feel free also to try different values than the three we have initialized the `learning_rates` variable to contain, and see what happens. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "learning rate is: 0.01\n",
      "train accuracy: 99.52153110047847 %\n",
      "test accuracy: 68.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n",
      "learning rate is: 0.001\n",
      "train accuracy: 88.99521531100478 %\n",
      "test accuracy: 64.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n",
      "learning rate is: 0.0001\n",
      "train accuracy: 68.42105263157895 %\n",
      "test accuracy: 36.0 %\n",
      "\n",
      "-------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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mXt5hJ3WgLj4xg11Flfzqja2MTIvjW7M6H2xPKRUaNBSCaPTgeKKcEawvLO3R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\nB1rXEmV9gVKsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2018a3a37f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = [0.01, 0.001, 0.0001]\n",
    "models = {}\n",
    "for i in learning_rates:\n",
    "    print (\"learning rate is: \" + str(i))\n",
    "    models[str(i)] = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 1500, learning_rate = i, print_cost = False)\n",
    "    print ('\\n' + \"-------------------------------------------------------\" + '\\n')\n",
    "\n",
    "for i in learning_rates:\n",
    "    plt.plot(np.squeeze(models[str(i)][\"costs\"]), label= str(models[str(i)][\"learning_rate\"]))\n",
    "\n",
    "plt.ylabel('cost')\n",
    "plt.xlabel('iterations')\n",
    "\n",
    "legend = plt.legend(loc='upper center', shadow=True)\n",
    "frame = legend.get_frame()\n",
    "frame.set_facecolor('0.90')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation**: \n",
    "- Different learning rates give different costs and thus different predictions results.\n",
    "- If the learning rate is too large (0.01), the cost may oscillate up and down. It may even diverge (though in this example, using 0.01 still eventually ends up at a good value for the cost). \n",
    "- A lower cost doesn't mean a better model. You have to check if there is possibly overfitting. It happens when the training accuracy is a lot higher than the test accuracy.\n",
    "- In deep learning, we usually recommend that you: \n",
    "    - Choose the learning rate that better minimizes the cost function.\n",
    "    - If your model overfits, use other techniques to reduce overfitting. (We'll talk about this in later videos.) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "source": [
    "## 7 - Test with your own image (optional/ungraded exercise) ##\n",
    "\n",
    "Congratulations on finishing this assignment. You can use your own image and see the output of your model. To do that:\n",
    "    1. Click on \"File\" in the upper bar of this notebook, then click \"Open\" to go on your Coursera Hub.\n",
    "    2. Add your image to this Jupyter Notebook's directory, in the \"images\" folder\n",
    "    3. Change your image's name in the following code\n",
    "    4. Run the code and check if the algorithm is right (1 = cat, 0 = non-cat)!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\misc\\pilutil.py:482: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int32 == np.dtype(int).type`.\n",
      "  if issubdtype(ts, int):\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\misc\\pilutil.py:485: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  elif issubdtype(type(size), float):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 0.0, your algorithm predicts a \"non-cat\" picture.\n"
     ]
    },
    {
     "data": {
      "image/png": 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EJrjJKBBKJwWniZCy0pscm0qHxP8Yix46a9lZcj546+OQ2PedKEprA5rKWUBB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XXCYNjhbHlMnufoaoJoG+JRl7beMBoNyCtQjkOdzshTywC1WoWW7o6uaVFXguc/02e9yXn3\n1fXOnxCg7RrhKXqxT9vCYpbj+AYH4bkhVm5xsH+ddXKMrMyHxwc++Cy2rfn6y2/y6R/4JNoZ8We/\n9zk8y+dv/OzfRngO6/XSRHIdl+GwT6Zc3nnvPs/evsnpyUPCwLxEPL8mSyt6vQGeY9O0pRH5WAHK\n9UgWK77x8ku4UcRHPvYp7r/xOo3acHRwk6jbJbBsCsfm6PpVLqYrDgPFK+dbvvbb/4w6q+gf9vju\n557ibHHOYnPGxUISWA43bh5xuTojmQtWWYJdW/iWjxsIks2aSlY8ceMmvRufZPrOFwzjKjul0RHS\nquhfb2jTIZFjs1qeQJii6wHD+JDFZo2qNP2eARNqdl0Vq6VJM4QfYtsa22rQDoRxjN9Zk8+G2GFM\nlr1DPOySlRaRrShy02ReLiviqMv9u+99S43Xb+ub/C/+0i+98OHnvgPbcSiqgsmwT1s1oAWdTpcy\nz0mzjGbnRxxP9oxFHRiNRqjWEOEEmuXFJWWZM9nbp93hZ2fLNaqR+J7HOtkiANcxMbk8TRmN+khp\n/sMHYQgt5IW5TTZty8HePkVlboO61aZSXtc4tkMcx8a8JAS6VQjLoSwq4qDHOlnhOi69Xo8wDNBa\nc3FySSFaYj9CygalNGmaE8Uu4/Ee+TbBi3zu3n2X3qDHZO8AgWIxXeL6Lk0rOTw8pColWZJiOyaR\nslgsQFuslmvqWpLlxrkahgFFke8WkS2q1Ty6/5AgCrCEZeicram0BraLLCscx2U+nxpkQ9PiBR5p\nluHbHgptlpFaE3c7LBcLXNcDrWmVIk0TAsdFSdMeLcsS17Moy4qyLMjrkuFgiCcs9q5e4fjRQ6Jd\nPNR1XGwb0qKgyHK00mzTLXEnZrEw5ExHgRf6FFVFkiZIbaBqeVpiWw7z+YI4DNkm292oyjPeUMch\nCA3OwRNG/NAIKAszjlssFoyGQzzfJ88NC1y1ZhmIUkRBSJqlaKVwbZtHJw85PTnm/ruvoVob4VnY\nGIhVKzw0GktZ5EoTaQvLj0iTHN8yUbtCpejGA8+hqhri2MV1PDbLOX4k6E+6bLY2pdriW5ownpDN\ncxbFDEt7eCiS5RyCiPXFBs920C0IWiI/Yng1I1nlFHUPWwmaQtK00JSSStTEnS5nx1smkzHZWlLn\nAllrgiDmyaducn75iCjsILRPv9fljVfexQ5jjt+5z+r0EVE4RgnoHYasZwumJ49YL2ZUsqLKMy4e\n3sOxNZUU6BbSrGA6nVGplgfTE5qs4cHpMefzgs2mZL3aMr14SFAX7O/5DGLQ+Ql500erjKDX562v\nfpP+lSHvvPsOKle8+dqXuNzklNP7bKsG8gRV1xRlwsXljO0qIfBdjo5iHr69xtEhQUcQO4KjyZDR\nfsh8eo6jQ8b9IXUlOD79GmGkaMSUO892UOO73L4V0lx2scqWTbYk7PawOyA6BXXTw3YucZsIXdY4\nlkWRlwS+y/LiEjeO6McxWhu5i+X7WLVmvmwIey7l+hTL9pCyA2mJ77lAQ7004hLXtlgtN9/STf7b\nO0JpWQz3JsjWOE2LssYOfUaTIWVtZtooTWC72MJiMbskT7PHpRipNGgDW4r7A/Ki3i3yTIzyQ888\nje/75GWB79jIpsLxfJqmZv9gwmy+JC8LLKXRwsL2LDpxjziOWS9XTGeXtLVCSbPcm8/nBL6L5zvk\nVWaIlkG0g1UJhAOrZMaNGzfQWhl7u9Scn00Juz6jriE5brdb6kYShA5x1OPevXt0B33OT88JQo/F\nbEmeJTy4/5BcFviBweNuki2+a+P4DrZt8eprb9Dv99mk613xZ0y/30eWFRenZ3ieT687wBGwmE8Z\n7Y0MddICuVvOWhijTdmULNdzwqiLY3tU0pDyoiCmlBX7kz3SNDHwtssZcWysSmXbPH4RVao2H85l\nSVPVOxWgKSG1tUYpTRDFNHVOv9dh2O8S+QFCNxxduUq3ExF1uxRNzfWbN1BNSxh42FqRVsZstF5s\nKHZdiCIrWMynaCUpixTdNOwPx/i+R1bktFIaVHWt8NwAqcxYp8oLfNcgl0VreEWu7dDrGHNU3e7i\ndY0Rkfi+i5QVSZJR5JJ37t0HK2BvFLE/tnAjZT7U7JbAlfQnGZN+Ra0sinxG1A0Zj67S6oBOcGh4\n4WlKKy02G0mZS249eQvbD9ic1zgqJNQOqIpK3ufg9pYPfnjN0a1Lhgd3wR/gK5c49tmqBqEjWnws\nt0GIkKbWBDrn8LBLb9TFpUVbCYHrs7nYkOVbaIw43N7RL7sDm4uLt+kEY6aXGbefuM6jk/colQJR\nszcZsc5L1uWSIIa8WHORf41CbDm5nPL6269TYDwOX3vxRb769T/h4WxN1ZR4VgLJK+xZW3Q1JZAF\n+0FO3CywdUpPGF/AurCQpc82gbpccnXvCCvJ+bEf/TTXJzE//PxHieyMp25c4da4y/xyhqMcAi9G\nuTZ4DfgCJ+wjy4ZXXzsD0VK2W3Rps01z0lYxW68YHwwQlubsfEZRb7D9S8TkLaqm4Sw9xRIteS5Y\nFW9Q6gLXWVOWc8qZJr8v2G4uaHOLJFfIsAUfotijaXO64z6OBWma0qqItpVY9cDITKyQdKspVUST\nd0iml+jAZ7NqiZwBlaPpDPpMp4tv+Rz9tr7J//1f/bUXPv6pT+M5Dr4fUtalwdRikRcFlmfT63Zx\nHZdwB7FyHPtxZT8MQlzHQLRqKdG0pNmWMIxxbYdkuyWKI5J0gyUsAj9gsVjhODau5xNGPtq2mfSH\nQEsla/KiwBbg2Ba+7xGF8eM6fhj7OK7DfLGgE8WM+wOKIqcsCuaLBXuTPTphxNn0gm4Ym6Vobfj4\nGkWeZ8hWMpqMyNPcPOE9j9HELGsd1yf0QjOq8gMsz2ayNyFZGX1ZWVb0Bz3SPKPOC5565gPMLqcM\nhkOkVOR5TprlWJZDp9cxasNGMp1dMhyNyPIc13O5vJjiuR7jvcluKb1EWA7dHQIhyzLkLs4WBqbW\nbaibIY00jH1HWBR5Tppn5EUOQtDr9FnO5/RHQ+IgZDafc3R0Fd/zcDyB54ElJA8fnjAaj+h2YsLQ\n5+rVK5xdXgKwSVL2JxM26w15ku2QuBI/CCjzksvzM+IwBEtjYdGNfePl7fZI8ozRwYRBr0OwW/La\njpm1W2j8IDSaNykpSnNoW47gxvVr5nVXFEhZU+QFAhAIsCzqqsa2BJZukE1FXqwRTs18viHb2LR2\nRZFr8nzNeP8q82lKXme4rubDz36IZJuQ5ityeYrnKCajCDeM6fQUrvCMo6DYoCxFutji9mKujGrG\nVxry0iUrYNS1ePBwRDIL0VHCwSBitcl5/mPfRRRuiXoaQckgusne+IgsS1mlCYGvEU5Jp+uTl5LA\naRlNBmzWJaWszN9vCWg080VOWTXYKPq9kPHRIZt5hrA1Z5cXXL9xg3S+5okPWZRyQaglniMIvBDH\nzbFUQZoe8+QNjyvDAV0nJSBjunpI02zodiL6fYh7iquTPQ73j/Atyf17x2SbjQk7bFOmeUbbNnzg\nzj5uk9DtVpTlFFdUBKIiL8548OCSolas1g+4cn2IrSUnZ3N63RHZeotrOzSuw0AHSHKKvDTL9e0W\n2obeaMRqU9HpenidivjmJXv2h+h3NPt9xXytSS7BFkNkU7BY1DQ0RK5PU7RUqcDTLdF+yzbN6fsD\nsAWtFISBT900DAcTHLfkznc8oKpC40jwfUbjM1ZTl/nymO/87kOcviJbwGKmGIxCZC0J3Ijt5ltb\nvH5bRyi1NjCxZL2iP5jgeAGhZ+E4cHAwZrvNKWRJ5PqUrURWFVEcsNlsaBpzeFpK89STTzJfzajr\niEF/RBB7JJstcethex6lZ7yuQrd0vIrh/oTpfEW/E1JXNSqODS43y+iE3V1G3dTZj66FNLWkG8es\nkzU2As/3EUpRlBnaErRAEMYURY5QGpQiKXM84eKEpukqm4o7Tz/F/ffumTKSUFw/usk2TcjzDNu1\nKbMcS2iKtOBSgec7XE4vcIRHZFlGa7da0407lGS89cab7E9GqKYlLwtGvQFxFLDaJgRKs1gssG2b\n27dvczldmiaq1nzgmQ+idMN6uWK2mHN09RClBUVukLFKN7S7lJGUkjgIGQQDju/dpzvoY7smWbTZ\nrOh2e1it4Pz0nM06pZElh0HA5XKJpTXz6SWW0IRRh6qo8W2fwXiC6wU4XsR8cUFRtUxGIy7OTol6\n5u/ZuzJmOAxZbhMc32VvcsBrL3+D2zf2WSUlgevsOOsWrnBZJwndIGIU93h0cgzaZjgZk6wSwl4H\n1/Efc9llXhLFIY4zxnNdkM1jdr0tLELfA0yLkVrtaIEuSmpsy8P3YhZryY0nrjA9vQRLc+X6kKYM\nwW44uDZiu4K69Ll3/5x+P8TvKFgPqVqFCD3yaYuDjaxsWiU5vOYhnA5WDEoLYqvPbONQlC11ong9\nKbAtG9ko2jzlogqwPYu3Tu/SkRa4Nt3emEkv4mKxxLZqwjAgz2ui3oQqPUFYHWo9QFiSvSsBXAqE\ndGiFohMP0c4j5GbA0x8+YjE7Qaxqiramg83+aMxob4gsFdPlOTcjxZdeHiOsu3jRM7RVimdvGR9M\nqKUF3gWyUKi2xPYl8SDk+OSSbhwTT2w2K82Hno5pbI9OHDK8NqZtPVxH8LQBa3J+esbDk2Oct2ou\nHp5CFFLkZ9yaDLjynSP80Zzs7SViv4+yI/pBwHaeYAuPrC3Q8y0XvRrfhlYKynVLFHaoVYMuGgLH\nJfRavD2HurpFlksenG0IYsmg12PUtZmuS1Adnr5zRDdyuXv3Ia1u6PVzfO8KgW5Jm4pNWTLwXGrP\nwbZann3+mPtvuvTiiujagOA8wxmXXJ4rYrfl6h0Hb3qFptPibCVZrvkrP3XIF/9kSuS5rNv0Wz5H\nv61v8r/6q7/2wp1nnmU0GjKfL+n1unS6BimQpznDwQDZtsy3a5TWCGHR6fZpWkUjW8Z7Y2Qt2aYb\nbMcjL0oC30dYNnVd4XgedV3hBS6BY9PtdthkCTaCK1cOjL3JNpHB2A8IwhA/8Gh2i7kwDKnyDCwI\nfZdWGzToerliPJmwWGyxtMCyLcajAVla0u13GQ4HFElK1O1QlgWOa+MIy4wJPI9kk+L4LhfnZ4x2\n6RjP92kbwwWvyoJeb0CSpJR5RRAEzJcz4jAkr0ocx2EwGePu4oEKzfWDQ6RSnJ+dcuvWTbZrM6Pv\n7ATmNJK41zWtUW0KHbbnorSmro15qioLojBmvdrQ7fURmJZfp9Oh3t10gyCkTHMqWdPUDVEYkWYJ\nd27fpihzLMsYpSpZEAURSZ7R6fURrr3DCEg8P0C1RgDT65mugGwlwvWxVIsfGNnFJq9JN1vWiwVp\nljLsj7i4XJDnOUJYjMYTXM+n0ZKqKkjzFMt22Bvv0VrgaAi7XfPKGA4QYBysTUMUhoYquCNVuo5t\n4rWVpJKSum6xHZfW1rieh2c72EITRwGOq0mKeyznJ1y7NqAq1+g6ptFdhPJoZY1PBa2iN6qIg5C0\nrhnoA2q7RtctSkLb2JRtiWV75HmNkhKEYDwcs23PmSU5Vt7FdQWONUDLhtayaPA56Pv0ohGilGgi\nQjdk1BviBQM2m5Q0bal1S9BRZFWJI2KEthFNSZk26EpSy4zQNX2L+SbBc2LQOVr6rJKcOtng9Lsg\nW575yE1aKShKyWKeoPBQ7grbjQiDFi0kLQ5hVJLrNV4rEbpCuB4AWlW4wjfWr8bjzge+m8Dr8813\nT+iPHT747Me4fm1M4cODt2b0946oa41MKlaXS7qdDp5tI1SH/v4TrFcNd7+ao6wQWRsw4HZToRHI\nqsbXFo1SWKlP1PURFihP8TM/87f42Ec/RufwBo/u34OiwXFdHm3u0gkOGXY0vY7P6cWaZBWQL1rq\nIkfKnIt0ji1jtPBw6OFaDa3yiLt7FKsBUaQI3YSqt4GLZ7jziQe4niRqCrZ2AF5NP6yYlw774ZBm\ncEw9D1lvJHeuO5znb3P9QLM4U9hij9Vq9q9/hPLv/eIvvfCp7/k+Y+/RBhvbqgZbOERBYAw+2y3j\nyQTZGM6373s4jstquSTYxfSELdhuE6IowhIWXuBSFBUaQ4m8nC3QSlDVkiCKmewfUJc1VVVSV5JW\nK5pGGbF4lqMwbPvrV49QNmA5+L5Ht9vFERaT/T2qqqJtFJ5rkyYJTWsy80maIAREOznI+3q7/mCA\nt0t4SNni2KbpmeYZnV6HMIyoG4mtDZlym2X0ohjX86iKzLRL83zHKre4fuMGi/mcg4MD1ps1RZqR\n5gW3bt9kNr1EWILBYMg2SQijkF6/j9wVPzSaxXJhbrFCMBqZwlarNEVemAKK0nS7XdCmmCJrSRhH\nXDk6MskarQkD86oy3J6S2WxO3dRkaUbTGKVj4PrkdYVuFaPhhM3WEDKvX79OmqaPYWm25ZCkKev5\nktVqSyVb5ss1lm4ZDIZsNhnL1ZrAC7FsQV01jPcm5LlZLtu2g7BMc3a7Qz5UsiYMI4RlSjpKawLP\nxxIC1/Mefx+e56G0JstyBJqiLMEyY57AD1BNw95kwvjqAfPFnJOzR8xnM9ZrgIJcxQhhU1YNe2MX\n2Vh0A5914mBHgp//Txw6/iNefXiAUxVYgcut60+z3Kxx8Y0PGEHdgNAO+4c9slKBjmirHD/oo6kR\ncUCtHfy2QiibphJ0OjZBqDm/mJFuK6KOz2x+Rp6XiEbgCAdXezStGUBJWn7ohz7L22+/ix36CAS1\nloyvbCmSCsePWCUtT9+4ypaavTCmdjWomM1qzXSxIKtcZG3TWC1VFVA1GzzloPBwrAKtYppcMejE\nlG6MF3T4zMe/h8ttw5OHByyP53z53ZcJg0fcONrywaeeZz4/5423vsA3/vTP+Mz37SPlu3zj69/k\n2Wdv0Qk7vPLqq3R7ISenpzjCo9t3uVjVgEWhJI4C1VYIS3OwN8Tv90wiy3H59/7mT/DXfuwnGO6N\nufvS69y+c4Mnru/z8HzBj//Vn+QjT13llXtvYrkNdWFx7dotZtM5dmN2TUmaU6QNVSUZ9IYUSYFi\ng+eMsWxFUgksO6G23+XD3/U0Vr9m/+gdknqPTdLjOB3w7JN9Lu+nPFzk9HuCLFqSzDws2aewW3o3\nFaqtuFzXpHaDe3TO8h7/+h/yv/TLv2myl1cAACAASURBVPzC7TsfoFG7W2ZVcPLoEUVp6Iqu53Lz\n+nVmF2d0owhPGLb46cUJlrCMBaiqaGojaY6iiFrW+H6wm1tL9g+uEIURV67sMRoNaSrJOl2jUSTb\nLek2oapqtGdz//iEMPIZ9EY4gU+Rlzi2S1aX+LaHrGrCuMNiNqdWkrgbYdk2w8GAThDheh6Twz1s\nLXBc13DFG4UrBEJbnM8uiEJDTYzCDmmW4Do+spFUpaniV7LCERZNXbHabpBty2g84uLinFZpPNvh\n0ekpl9Mp12/e4PjhQ2qlcbDwopAiK8jylG7g41g2pazpd7tcnk93KkEPYUEcR8hGEfgeq/UGrY1F\nqNWG9W6QBmts12fU7VHWFZv1Bt/zubi4oNvr8dqrr+L7AQibbZKCZfg32+UKrTS9To+sLAiDAIUp\nYaVZRhzFFLJiNp2x2WxplCLPE3pRh0rmhHEP2TbYtjmkSikJXYc8TXAcizTNcYOAwDXpnzCMKJsa\nR9j4nkfg+rSWEcXPlwsODg7MYtR1yfKcxXJpWpY7+YzjOFRVRaMt6rI21EDZIISirRuwLcbjAYHr\nEEURVWmWylcOD7k5nuA5fX7409/HzWe/h48/9SG80YhPfvR7+MuvvYrnDfiDv/Sp86dZbLf0oyE3\nnnyGe2++iXBCItsgBsL4jL1r3833/ciPcuP6Uwy7K+6+fUG/M2C7LfE7Edcme6ybDf0QtLDQrk2R\nmAX0pz/1GbTvgu1C3rJJUqrGZruq6UcDlusN26Tig3u36Q4HDA/7zE9nfPSDH2eTnqHWLUnT4RM/\n+AzP3PwwdbZASoHntHzyez/N3v6IsBdx/N4jhGywsLEKhTNxef77f5x333jLIESyitu3r3P7qSdp\nSwvXUdx85sO88sefZ7Y85wO3e9ijiOef7BKEQzYnEatNQrrZcDGtyC4SphczvuOp53jtvRPOT5fc\nu/+IJ67fYL3O8COBrAVHg+sIvSbfrnDtAVbg0lYaLSWbLKfVLlWWYylN17NxgxEvffXrvPbWI176\n2tf5wh99iZPljPuvvcO2OKEobcIaZBLw1uv3WTys2b/Tp+d36I88srTLZOhRJBVh5CI8n7bd4HQ7\neLJDpxsyuNFFyjXrRy2WttkWa/a7+7TJ6P/m7s1jZEvP+7znO/tap/bq/XbfZfaFQ85wuIgiRSey\nSCkI5USKFBuyxBgUEks2hABKHNgwYVixY0sKFDg2BEcRJAdQKFqBtVGUKG7mvs9wNs7c/Xbf3mqv\nOvv25Y/TGjMJLBFCAsQuoHEb59bSS/V7vvN+v/d5WJys6fZc6qjLbKnQdzYoY5/1ZIErChAG2SxA\nGDZq6KFJhcn9fw9y8v3hSP4XP/Uzr7PdDdtCVXRc22a2WNDquMgKFtMFltVYfRzXx1SbsfPR1iaH\nh4e0/RaLxQIFgW1aWG5jblIVHdM2mgnNC+9qnhS4nt30lYsC03BxbYdVuGZ7a8hsMccQChUSFYHT\nabGcL1CyEt1zLywvBrPF/HUtHXVj/9FUges0iN69rU3WcYTteNRVgURBKBL9QjU2PR+/jtqVisDQ\nLVaLBUmekBV5AwRDNBuYsyllWXJwcMD9+/cbwYRloiBIwqjJ/5cl3dGgQSSvE1rdHicn99F0Bcey\niVZrLl0+4O7du+xfPiBaN1KV+XyObVvsbu/QCXxmixWreSOLsCyrSSI4Pi/deI3BYEBRpsxnS+I0\nQUVnMj3n6tUrrGZzoiwDoaJe+EbTNKHKC7qDPveODukEnYbeqevEYYTruuR5TrvdZrFoMNCmaZJf\neHjPzs4akJgQOG6zkZpnJbbX8Pstw8RxbObzBaquvU4DtW27+dxxCNcNhqHB64Ln+Q1qIm/y8FJK\nbNsmiiKyvCaJYmpFMJ/PL3DTOY5jYds2ge/ieQ6f+cxn+NbLz2MZClWZsFwvyNsjXtDfy5Z+zEzb\nZVZWHHzhf+LazoPMlSmb249wNrlHry55VC747ERnto6ahYlWY6gZhYCrGzsU6NQVLOdLrly5Bm7A\ncLDNc1/7BJOz2/S2H8HA4frNW+z1K0x/g73hBmldkhUFs+Nj/F6HfrtGFS7B9jYdy+Rffug3sVY1\nw60NDssJHdHDaTl8/lt3GLQ9Htw74LmXnsfSLbYudZjdu8UjzzzBcHePMnbI64rDO2ckSYSJxtbB\nLqrm8Pz513jPW95LkRVQ5uSKglIprMqCT3zrCHl8k7d3Kia5xmndx1BDDM/BZBNj+RrrYZ9rts6L\n916m3e3ywAMaMgn51Cfv8sM/+n6+8dWvsD0a8kd/8FH2r1zm5HSBMAW93iZ3n3+R4c4Weil46PIW\nlx+6CvOMP/7mc9y+OyaL19DO0SuNohIEnsvu9gChety5echgzyOZCJLeS5TZkPDOjDLX8TeXzE9S\n1HxArWRomsbZ+X2KQqeWBQ88somiqEjFRq8DSiPEMUK8gcXx3QWPP/QIkXGL+y9fcPvN+6j1Dkot\nSOIMHZM0KUmrjDBZsLPlULozUqlSj332DpZ89g/kvw85+Z//YKszottr43gt0jhhXSZURY5S1VRl\n0yYIugG2ZRGHCRqCKE5YrZZEUcqwP2A2m9P2mt4rqkIcJ0zGU+K8YDGfoqg6J6cnqJrGeh2yXC3Q\nVQ0NBd3Q0DQFlEbEe3x8iNsKMA0TxdApywrbtKhriWGZJFnKYjlho9cnjWMs20JHoNsGvhuwWi+o\nZPVv5BZZQV2WJEnGcrWgyPOGeqip1CjYnk2WFHQ6HfIsY2M0xNR0LMPGNG06vQ55UTHa2MTzHTS9\ngYpt9oZIAUenx7iui+f71HnT083yAktV6PQ6VGXJ+fk5mxsbhHGIppsMhn12NzeJ05TtjR0UVcM2\nHdK4ybMHQUCaZSyXEU7Lwbc9xvMprucym8yoqprRaIvpfIxt2SymC0pZU9cVnU6AaZosFnOclkeV\n5dw7OqQ/GFBEKbUCvVaAZhh0Om2CoJm+fdf3vIvrN16lrBrBhqqqJHGClDXL5ZJWywep4PstJKAb\nejMcZVrEWcrl3QOKsgApGQ4GVFneKPGynM2NDcqiQBEKAkFdV2R5jgQ0Xacoy6ZFQ8N8/xNNXqMi\nFxcuTpV4HeK6HlmecufoCChISwPFURBRxIN8kQN1zkb6OYJXXsKsAxJrhix1puEZLa2PppusO1vo\nuoFmarieQ+AMkJWJ5/tQ29RVhamo9De7qLlBma9ZTu/z5GOPU6QKH/zbP8MnPv0ZrlzZIlqVXH7s\nQYxaYLnNlaXreuztP8CdW3cZTwvCyXWS83Oy9ZJzBKs8RczWGKqF0b3E0O/i37pNx9Fp+bv0N9q8\npno8fPWNHN2a0m0NKSkoi5xXnnuRzPOhSnngrW/ihc9/GaG/kS+89AU+94XPI2drLj/7DJ/40pfZ\nGwy5rCnEKYzvxAwfUpqT6GzBUM/wWDG9e8KTB/t8+utfIix9Sm+Lo1nJV+5pXOnuMOp3iaMIu9Wl\nMxjR7Y7wWx6lrvKB9//XfPzTf0Srv4myOIPFOenhbarpKben97j01GWuPfEGwumCawcPcnp/zNNP\nX+Z8suTKwQ67+xvM76+IqhOC3SMGypu4d/gqQrWwhEMcptTKmnQNnX4Ly96g3dXQNQOBJEpLFBMU\nd0I4zRlcOSKdCwyj4JVvnnDnaEa3pZIsUjb3tuiMlizOYlZTnSwrMUwHxzYI3B7ns3McrcuwmyO1\ngrjQmdwr/91v1/wP//jnP/jY408Qpxm6piGFwDAtLLXZRFVVQS0qAtflfDZD1DWrcIWi6Vy+dInV\natn0qauKJE0wNJ28LFgvV1iGSa/bYzqbUZUNBCtLU2zHwjId8qLEb/lMZlOiJGEyHjcO1qIizVLS\nOGM6HSMk3Ll3l1pIVBWKPCNNChSzkZK0Wi2iJCYNI8qqxnEsTM1qsAmKQNcanK/ruww6A9I4bqYp\nK0mWxWRpymw2bQarFEGSpYTrNUWR0Rv0ePX6a+zv7bOYz2k5LuvFqiHn2Q6uaeD5LWzXhrIRXVdV\nRct1iZOoYZkAO9t7xHmC5/vcuXsbISWqohClKa9862Wi5YKz8Snns0mDITg5a4z2ukpVFSxW62Zj\nuCjo9frMZ0ui1YKW6yOFxHFtzs7O2djYYDabNfxz3SNZr1BtC8/yyOIYt9WmKgv8C+EJCLI4ZhWF\nhIsl55Mp+5cucXxyQlkUmCjMlgtanTaGaXE2Psd2XSzLRNd0fK9NUqQEfot7d++A2qzeVUWQpCkt\nL8BveY0oXdfRtQbNLFAxdBPLNpv9H9shS0uEolBLSZo0Ecq8yKlqKKocVVUASX/Yoy5K7tw/4vR8\nTMu20BWDxXRJHhas4xVZvsBueUxXS0Tt4RgWvqIzbPu47Ta9/oBvvfwieZyQJTGlTCkr2ZiHqMnq\nEllLHNtjPp9heXbzc1qsKND4yhc+w/T0DK/dYjKd0ndsVE2lrGtMx0bVFV6+cZ3lbE2Ur4jnCqsc\ncmFimm1qUeAPDihNHVWUzOcnWJc2OV7Ned9f+Ut0Oj77To+WE7F50OcTH/sjru5uc/c4Ig1nvO0d\nT7OcL5sEW3fISkYc+A7XtrfQDY0Xn3+OwDb5+me/wL3JjCLUONl5C5eVgnvzJX/nH3yQP/7I53j0\n3d/FzmaP2y+ecKSBUceceh6t0zOykyO2L+9x79VX+OrXnuP29VeZnZ4wHY+JZivyBL750vPIw3Me\ne3CH8/tTcsflxiJlXUjiWuOZd7yTr/6rj7Gz2SG8d5PAhaxSSdOSk9Udzk4TJG2idYxSC27evoFe\n29SxShQlZFmOaQY4tkmxLklqgaZVxCzwVAvd6hL4OlmRc+Xqu3jt6yeobpeq1pGVhVGp4MyZTB2y\n9D5VtMlklmKhsVjOyfOKuogx7aphJWk2eplhZxbRUjKdFP/uF/lf+IVf/OBjTz9LUUuqIqXKm366\noPFkapbBxmgbWTUY0qqqiLOUwPcaTsxkTLfdIYwjyqpECMFkMcN3XPIiRzVN1qsltm1hGi6dTo/p\nfIFpmeRZRikrXM8jSRL6/T6KqjKeTFAqiVBUgk7AyfExluWSZQWKomMYNpbdoA5M02ymGLMUzTRY\nrhaEUcR0NkFVFGazGWmRk2QpeZGzWC0IkwhUQVk24DEhoMpLPM/j/v37TSsjSXB9H11Rm8imZWBa\nBtPZjCgMkQIW0ymKkMRRwng8wTQs8ryk1+vS63TJ85waCNcxVV0wGY9BSvQL9EJRVVR1k+bxWi0G\nwyHr5YrRaAtUgWWZyFqianozMTyfIyWcT87xXIckjjF0jShJMDUL27WIohhDt0mzlG5vQJJG+J5P\nWZU8+cQTLFchjuc0G8F+i+oi5ROnKVnZJHWKomhy6boOmorXDsiiBMOxGQ02KQuJbmoNOlgBQzUv\nCraDaRoIRVDkBQC9fpcoifEcl/iCi6MoCrWsiNKGvOnaFlXZ9P/LMidJInRLJ0qzhqFe1mhKIww3\nTZX+YMA3n3uOqqxQhCBNc9pBmyTJWCchApM8A9vT0OpdKiUkXeXoloGiWyzm5xffv09/OOShBx5k\ne2ubvYNd+v2A0d4eQdBnPZuytbtNr9tG1SvydE2/10MiOb53imUazXxDpbB3cI1VkUFVkUmIFzMG\nw22kkFSVwNIyFMNoriKyOdEiYjTsEgQB07MJpdCIk4SyhMOjY1ShsqMLPvLHf8wb3/AXOJmc4Fge\n9rBDL/C4P4s5OZvRG/VpWRUHumDzYES7HdAdDHng2j7DIOCxJx/h8ce2yZU+D48ULj004NKlqxha\nxtVHHue3//iLKDnYmk68PkVqBptJiTvos7u1w6OPPMTZ4V2uPfwwb3/Hd2P5HZ79rrcx2Bhx7bFH\nsVyFn/p7v8RrX/4wh7OQYDDkXELdDfBMl/PjU5SgQ5bDXFOQqss8liBMpqGJQckyzlCUgrxQ6Q7h\nxosxnZGkiGpqw8KsJcJopPLSEESzhPf/xH/FrRe+TiFDsjrGsbdpb/WJxivcvmQdrdBNSftSMxho\nqw7FKqDMc3Z3eszDEnKX0WaPMAyRouTZd28wvdnHjNcEUidU2ozPv7OJ1/9fF/mf/8Vf/OAjjz+G\nZziMRkMcx2E4HOL7Pp7nMZ8v2R2NULSaXrtLy/UaY5PtUlc5G/0RWZLiOW4zHGVaDURruWR3b5fD\ns1PKJMUwLRTZSEou7e+xt7fHoN2hP+yTxgmOZXN+dIJmmViWRZqntIMOi8mMre09qrqkFXjMZnMA\nDMtgvViyvb3Nar6+ODEJNF0lTXI2NzeIohS/FTBfzOh1uii1JMtKPM9lOV/RCnyyrAFltYM2aZZh\najppnjGbzdBMi9PzU+IsJU0STu8fUyEJV2s0VaXf6xKnMcv5irIu6bYD0BTSJGV3ewfD0NFNg63t\nTXy/he97tP3W68khx3MxDAPPcSnriuViQdBqNSxzw8RAJUpiVNUgy5KGepnkGIrZRC1bAXGSoZka\ny3CFlApxGqOoAsfUMTWTwcaA6dk5aZZCXaMZJoimLWZYJorQODs9xfVc+p0BcZSQlQ0R03d8oihm\nc2ub1Spk//LlC1xwgq6aGIaObZl4nsNqvUQoUBQFQSt4/XegqgqqppGlKaqq4nguEklRliiqChfJ\nnqquKMuaWtEQKMhCkhcFWZ5TUVDWjZO13x82Q3tJymy1YjqeoUkQus5stUZJLQwzIy8U5ktJlc0Z\nji4TxSGOrzXQr2VzJVZGBVmtMJnPmC+WRGlCLTVWq4hVUiDLCtOwkapKlguyrKCsIU4r4jih1bHR\nlBrLNsE20PMKy7KIyhy9rEh0n8XZKVm8QC8FFZLjyRlCcVCFJMo09JaLKnPe9KY3MB+f4/kB0XpO\nqNe88toN3vbYDl+9NWdr94BCkfjCZLQ74Ojut3jXd303u5tD2n67eS85PnmR4bom1CBsle3egEWW\nczC7z4evf4OevcELz3+ZL3/1Oi989rN0NMHjVx/l1vlLtOsW+nDAm9/+JO9+9g0sT8+5+vAVHn70\nGdyWR6sV0B90sQyN2XKJi8OnPvlpzlaHvHpjybKG8WKO5ngUq5BWy6PfaeZfVENhd2OTaJ0hFQXT\n0uk4XUpR03dVpNKi3dnk7OSI/cs2gh6mK3GNANO2EaZoeuh1htMzeOn6lwgnNpptYyltciVlw+/T\nH26wntzhYPsayfKcIlboeQ+SFzlpEkPtsYoL+i2B7W6wXMw4G5+x98AllmcmWQplvWRVOqR2zfx0\n9e9+kf+5n/u5Dx5cfpBuv4vjOOi6iSIlVVGgaSoKEsd1UVWNLI1RTYPdwRDTMKnzHBAYlsn52QTP\nd5ueawmjQRdNUxl0O+xs7rC/dwnLcekFAbpyof5SGkSwqug4totqm5iGznq5Igi6TGdTXNelKlI0\nU2v0cE5TGKlr6gpk1YDPOkGbqq6xbYeNzSFCQhQnROHqdSlHKevXc9CdVovFck0tK6qqxri4IpCi\nQSRblkW8XNHpdinKgqqsyJKU2WLF1sYGaZGDpl4kKAoUVaXMmoijbdncOzzk6N4h8TpiFYbURcli\nvmAyb4ajhIAiqxACpIDp+YyyLMiTlCRJG4OSkE1/P00YjYYcH95nvpxhulYD/dJURqMBqoRrl6+Q\npM2wjlCaASVVCDqdLtPZjOVyiaoomLZJWRTUdU0cxVR1M1HbCtqkF4rGJIpJwhW2a6NIyXy5JIxC\nDF1ntLmBrqooqkA3DYJ2GwkMRyPSLMNv+RRlie+5xHGEY/uoanNC0XQdQzUZn09I0xhN02j5fnPy\nUTVsy0RVFKqypChKiqpCVhWKaqAJBc93sSwNyzKI4wh5se8ynk+wL6xYFDPUQrDdMqnMlFrY6JZO\n4HVo2S6e1yJKcpKkQnMt6rpC1S1KWWNqBrXQCBcT+kGLYafLbHHKYpnS7zloosa0HG7fvYuuqlim\njUCg6haOa5FVBa/cuMmovwllyHw95XfvBdxZtLi58RQPjFpsPPosTz3xVj6ZvoObwSOcyMfpxa8x\nXR7x1re8jfH0nDe/4+2skpoHdncY4zPs6TxxZYuXXniO/auXcXWbje3dxrpWVFSKJFpH6K7VRBl1\nlZ3dHcLzGWFaEEwW/POPfpGuVfDU02/kkd1dbh4dEq/WfN8Pfj+GGlPWDknfY39ziz3bRrgqp5lk\n2GkT/5N/gTbaZF4nJElCnAksy2bkmLx681WUqKbb7zNezrFUG69SUHSNfJZzbzXDFhLTsCjjKZMw\nwTA0XFdDrUo0UyMvChzPZH97hKpITool5tkMxYDj6wuo4WR8n153QNfQ0FWHMKkoF0vcjoG1OeTd\n7/geBlsdup7Cebzi3tEdNvf2SfOCchZxcOkyX//6N2gFDq4tWMclpqg5unvK3vaAxx++RG1VzMcz\nfiDX+OlbGb8lC1ZR/P/OxKsQ4n8FfgA4l1I+dnGsC3wI2AfuAD8spZyLZmTwl4D3AjHw41LKr188\n5q8Cf/viaf++lPLX/qzXNkydg2sjZospp5M7OJaLgoqp6QwGAzRdkKURlZCUWUxJgaqYzJdjpqsZ\nQmsAUv0Nh6DlkWdlsxKPcqRSI+ui2byrY5ZRMznrejae0qGuCwzVwDYaho6mB0hJQ1qsJEHLY71e\nMuoPSKsGd2rpOqgqdQWVWbG5NcJxLXrtNkJTMQ27wRIPBmyMtojDkDhr1HrL9YpW0GF9Ae9SqEHV\nQDTgqjiO6Xa7JElClqa4rstyuWS9XrO5uUmv3aHTG3D//n3yomK9XDWmedNqev96Q4k8OT5j79LO\n6xu/0WpNkabUQtDt9wnnS2pDwXZVbt25zaXdPfy2x2y6QLdMdKtpwbTNNvE6xWv5rMI1hm1hywpb\nMzg6PHmdsa8ogps3bzJfrVAUQbvbZb1cYWo6y+WCLE3p9boEnUYhqBkGRVqiKZJur02e1Zyc3Gdj\nOGoctb0eqtmgK9xOgBembGxt0gnaTE7PGY76mKbJZDalrmtMs8Eb/4lisSxL8jTDsnrUlSBM1mx1\nNonjmFIW9IbdpsUWxY1JyXIwLJVKNgrAoiqpZH3hXQVFSmzPpt1uo+sqqqqjCI3ZZEYYxgy7AxzP\nokgrclSEnmNs+GxkzZXo9taIIssZtdtkecLm5pCyynFth0tXL6OoOi9/83midcz3/Yfv5KO/+zvI\nLEVv+Wh2QKdlcffoNpudDbIso91yoazRDQWpGPhBl7u37hIVBbWi8upz1ymLGQsBz3gO49mCvek9\nnnj6xwjRebk6w0zvYOkPoRRjNrY6PPLIG/jXn/8mZ0fH6O0WP/Sf/mf8/od/k8OTkKfeepmXzk2O\n66v8hZbDqpboqkOe5ChCoFag2yav3rxLlsVcPrjEjVdfQ1YKIq/4F8+/wvt+4D0YGw5f+NgXqIoE\nLzB41w+/D1UoVInK+OQUvBrR8lBaPdLTkMdGbcqzMbe++woDr0RkJpVWkuUhlqJwLksUFN7+5jfy\nB5/6PB3DoOO6HC4n9GSLczdm3+gSyYI745ArGwGPXh0SpQVFrYKV8srLN/jedz7LA49fY7ixy3Nf\nH3GpuMTT73873/jc51GvRFRCQXoOz/6lH2G8DvnG732c+tXP4+1ewt/SCBPJ+KMvoDgKpiwY2CaW\nNsKdB+z02uj7I8qWwyNPPE49y1nM5nT7PUwn4NojNmbbZ1YPGJ++iBINceWUD20sGeY+R39WAf2T\nGv5nRSiFEN8NhMCvf1uR/0fATEr5D4UQ/y3QkVL+N0KI9wI/fVHknwV+SUr57MVJ4avA0zTi8q8B\nb5JSzv+0197Y3pJ/8Yfeh63qeG6bxWpJXeRUdY1pW+R5ihA6WxsjwixBV1TSKMT1PfI0IUor0jRG\n11U81yXLMsbjMRvDEVEUMdraJFsnLJM1fa+DNCRqfVHUTYPAd1lHCZWEju9QFjW3D0/p97voosZ2\nTAxNY75YMxptkpQpZZKRlim+3bSUdE1juWrIj8vlHCFUDMOiqBsvZqvbI1mHbG9vUwuoc0lVZkhN\nQVSC8XiC5bskYcpwc8Dp6QlCglBMVKUmjSKKrGAw2qAoMgzDwrRtVCFRNAOllmiOQxiuULUmJvjN\n556n67fpbY2aNIMU5GmGrAoUXaXd7jCfzxufahCQZ2ukqqGpNr5nMVlM2egPSbIMXddJ4gLDVMjT\nlLICXVMwdIfx7JT7h0eM+gPyLAUhWKcxWdI8rtPrUhVlAyHTFDzPayKouoIiNM7PzxspR5ajmM1k\nZMtvs1zMcDyXcN2AzHTTII5SNjaH1FmBH3ToDfoICaohMDT9dRBduo6oREXQ7lIVFTWycXeWFbWm\noNCgGlbrBYPBoHGdSkm73eb0/Jw4L4jXIaswpSxLTF0lCAK63TZFUeBZDl9/8UWi1Zhv3biFWqS4\n7SHT8YQyDHnve9/D0eFtrt8/psxL6krjob1tbt8/RJEltuNTKjrRcsbWzjZ+r8fWxibtdpuyrnjp\npRcIk5QPfOCv8c/+2S8znsxwpUrLdrn16suYoz5qraAISSEyup0+QqkIlyuO757i713hm+EDvO2Z\nJ/nGqyXT9QkPz/43bshnMUabZDJgNvkGI+9Rst6IB1af5C1XC1aZxl6/zU984Kf4yb/xs7zx2Tfi\nqiq2a1FVBZqqUic5qzSn3Q2IkzWaapOlS/K85Ld+6//AUlz+8o//FcoyJ0tSpKLzhd/5A0YPPYKv\nVbx2dMh8Mqc/6vLsW54hTUqu7AZ8+Hc+xt37d3jy8mW+53vfw1L16QUpyc2Qj3z6Y7z5zW9G01Xu\n3jnG3uhhqBqBZYBr8NXPfYa9wdu4d/I8vudyengfXSicpUv2nQErrUQkMVGlo5kqep0hdAvbqrm0\nc8AynNLtCxZ3VljeHkl1RhhP0XSPpBa8970/xq9/6DOE0Yxa0RnqBXGUEVsaXstFkyrlZI7UTFZB\nhTONqIROqYWYZsDkfMFmfc72w4+wmK9pW5JCD9CyhBdP7+NXGSJocXL2eXyxy/FZytbQwbEsvvnc\n899RhPI7yskLIfaB3/u2Iv8qBwRfkwAAIABJREFU8C4p5YkQYhP4lJTyQSHEL198/hvffr8/+ZBS\n/uTF8f/L/f5tt96gL//az/xNxsenZEXOaDRgOV+hadpFjj1rXK9Fhue2SPOEOIoA2NraQlF1rl+/\nTrfbp8jj101D8+msmWIUoCtqwxHPazRbJY8SiqJic2+LOitILyxErm2h6yaqrhFFER3PQ2gqWRKR\npQW6ZaNpyuuZdCllU5wUhVarzen4HN+xm9WqrtDu9psp1CRFbzhXSFUjT/IGgZDliLqRaZeyhAq8\noEUWJ9R1ia42MgXH8bh14zr7l68ghOT8bIYXBFR5TlZlKFVF0OmCbCZoF4s1ruuSpRFRkV1w0FWE\nEJRZRl6VmKZFkiS0Wq3m+yhLUARtN8B0TEzbQlYVqqqRpgmO7VPVRZNhLwtOj4+xbYez8XlDnMwL\nFEVQlg3zRjcdwmiF4zWgOE0oOL4HQFmWqHrDBrpy0PTZqWom8ymHh0cN5lhKTs/P2NzcRFY1y/WK\n4XDIcDhsaKTTKZvbW6iimXauimbTXdN0hCrQpCApc5Ikpdfrvf5+WKwWCCkwzQZq5rpu01qLokbM\nnsTMlitWqxVZWlEXJarWXF04joXv+5i6wee/+CXu3L7HKo5xNcEDD15lNpvx4nPfwjCMhvOex6Rp\nimX7FEnMwbV9ui2XqMjY2buM3+oQxzF7e/s4jnWx/6Gyu73DnTv3ODm5z3K9wvE9jo7OKdMF8SIl\ny0N6wyE3btwgizPa7TZJVpCGa7I4w+93mdBDhGsO6dF94D24L/09fvqn/y7rVsDP/drH0ZcGf/2/\nfDPaJOTW7W8RLw9pOQ6+5ZIWDn7L45//6j/lP/mPvp/dS/tUaU6mSHq+zyoOKfIUw9AZ9ja4/uor\nFHmG124jFAM0FU1RoK5IipJP/e7vU+g2KjWPvPFxxvdPCHoDFApEJXnrG57k7hf/iBufvcHiYMT3\nfN97uPHNL/HWd303J/dWhHnM/fsn6LrKaDRinWWYikZRZkznc3b3tnn+uTMUsWZ7q81qNicOI46T\nmidaghulx0CNuRdluLWCKQSa5ZFSMJ+d0dtyudzWOdi8yjdeu8XR/WM63Ra97R3yPOe1jz9PFwVV\ny3niL/8Ak7EkiWKyaMU0DVGSgirMWKxL8vE9LN0hL2tMoWJ5Dh0hWSdLqs0ule6QKxV5CZ4iqNUa\nLddBdcCewlphMr4LrQy9eJJXXvnYd1Tk/7yAspGU8gTgotAPL45vA4ffdr+ji2P/tuP/j5sQ4gPA\nBwA836NIYkxLJylzwgsmStBps1wuEUpFuz1gtZwTxWs2NjYIDZPTszHTyRxFE9i2zXI5Jwh8XMch\niiLSrCCvwLagVm0AFMtiNhuztbXFfLpgMplh6ga6YSGqhu1u25IqqTEMjSjNiOKUWjatmvxCGN3t\ndlms1rRbAVIRSE3hdHxOEARYlsFyucQUJvfvn2BZFmVdIVAurkJSHMdivlghpUDRFagaiXm302Yx\nm+MHLZKkkV6XdQXUtLsdZrNzKgRVnTFfZCyXS/r9Lo5hMpkeg1TQNIMkiSirNUkck6QppuOyMRiy\nWCxwHA/fsImihN3tDc4mYzqdDr7dZxWvUJQKodRMz07wg04zIJRlKGrN6ek5/X6fJEkajv34FKWu\n8SyDs3BJv9NF1z2EhJt373Ht8hX8dvD6CdG17NeBdKZrYlsur7zyMuv1moO9S0hFMJtNybKMnZ0d\nHnjgGrPZjPl0xt7+Pp7TTAobmsF8uWA0GpEUCXmaUdYNWtoymuK9WCxwLBshBId379HpdcnyHMMw\nmxNVmlIUBZZhvj4Q1Qy5qQCYmk4pSqQQ2G5DIaVuNoxL0chYgk4f1S2Znxzywsuv8Kan3sjVa5eo\naoXx5IRaWCiyJktT+kEHIUARgv1rD9LrDVDVCwkMVRPp1HQAXnvtNVzbY9Trs7U54uTkhL1Rn+3B\nQ9w4OkLKjHt37/Pwww+RJVGz31OWaHVAnBes5is2lTNeuvt12httoo9+gl/+l7/GJ77wIuXRXX7y\nSYNP/euv8OTmuzlCI4g7kJxx+coDKAje8/0/QlxFbF0ZoshGxuIOAoyi4Hw8ZX9vl/F0Ql0VmLpK\nTs2la1cJFyFxHqNJk1WRY2kqmqrxju97N4qwMUyVdZ6y0e7heD6L+Rm7W9uYLY/xbEr/h99NMM9Y\nxCs6W/vcu3XK0Ve+hnVtl/4gICtSXn3hBbTAZWPYYRAMKaKEz37qE0ynY67u7XN4e06RNXtaXTOk\nXQWk5SHLzMWMF7Q6AZ7f4D36jsb8HjywfYlsfo/arDDVnOGwxauv3uX27bs88+xb6K9TTNulTEOy\nuoUe6Ng+1HqJl1S0NZU4VSh18DSNu7/662hCUpWS9OQUoftoRUjw1AE37yzYvLRHVUWkFag5oMco\nukArLIQF7/sb/5SP/8qPs3OwxSuvfGfF+s+7kl9IKdvf9v9zKWVHCPH7wD+QUn724vjHgZ8F3g2Y\nUsq/f3H87wCxlPIX/rTXHYyG8gf/8x/CMAzOx0sMW0HXTZbzKUEQoBSQKZK25xMnGe3AR8iaW3fv\n0e322Rr1OZtMMbQm0ugHHsvFAts2qapmiKmoajzPo8hS5osQr+WiSPA8j+V6hWs7rNdrev0hy9W8\nMekYOufnM8o6w3V9VqsVhlAxXQ/bspCAkAqGraIIjSRegdBBNJHAYadDXUNWVpycHbG5sUvLsamq\nCtP3ieZz4ixnczjg1skRXdt7HTymaQZIBaFUaEJhY3uLsiw5PT1lNmtE4HmegyJZzhcEQQf9gmCp\nmY3cwzObCU7TNCnqiiRM8AIPXVfRFBCqyXodXcjMJ2xubiKEaK5gel3qUkJV4gXt141DUKOoOtPJ\nhOoCuWAInfPxEe2gzyqJME0dQzUwDQ1FaJR1RbfbbuYEXB/LcpBVyTIOMTWVs0nzewYF1VARRUW4\nXtLu9BhsjFislsRhBFWNY7l4LZ/5co1pW/iW0wxM5TlSSGRekhY5w+GIo5NjHMehrips16bMSzaG\nI8IkJvB8dEUhLrJms19tiqtEIYrXzGcRi2hKFjdS+G67g+c5+C0X13VYhhmf/vSnGZ8cY1kWDz3y\nMFIKVKGwf7BzMdRWcv/kuHm/Khqz2Yzd7Z1mpiJek6Q5ipDMJwtW6zm65aDUzd9EKQs2NjdZrWJO\n799DlhVBt0N72OfLn/sSaRZhGCZZkqCqGlEUkisxndzjmBpXlZCljXhC18HUOD+b090cMTmb8Bff\n9Xam6xXPPf8CV69eZe9gn2SxwPZb1FXF4dERb3ryDSxmc0abw+bkpxks5hOkpBHkqAovv/wyru2g\nGiZhEuLpHrWhQ5lh6CqqYpLlFR3PoJIqQgWh26iyApGBYSJWTXCgzjPyAqIyxzUsomWTGIvCFbfu\nXKfGwNYMTk6nqIaK7+hs719m/9o+H/ntj7DbGWBv9HB1wXy2pioqkjSnWE0Js4TKsMjXa0oJml6j\nGR5Wpw9JRT4c8F39FT/yox/gf/yff4XNns/t4xnJ4ibu9qNMP/U1aBnsv/O7yISNrA00QyXXXbSy\nwNMrSCsy22K8XDH/w4/QWkmSdk4eF6jGNmY2I9lyUNw+i1WEqhcI1ePxNwQ88ez7+MrXjrj5rY+i\naRp7G2/jztFr/OD3vIef/Vs/9v/pSv5MCLH5be2a84vjR8Dut91vBzi+OP6u/9vxT/1ZL1LXslHx\nKTaWleC6Jq7VokwKhv0R47NzukGAqqrYsiLNMioEo9EmipC89Mq32N3dpSya4nr37l0G/T6T8YJe\nr8dyFaPpCuPxGM/3MSyd9WLZ9NfTHIHa/DEYFkLVcCybxWyJogm2draIw6hRp12o9BazOa7vsV6u\nGoiX6qCr0G73iZOkyfEnIWuzeYyq6gx6G6xXCxxbRwrBajnHskyIU6SUbA9GhHGErqjYjkeappia\nyvHZhP3dPW7dusOoP4CqxjZU0iyjKDJ002AwGDStotNTZN1YeExVYZ6VDTs+ifEdl3av3VAms6yB\nXdUlw16XvCrpdHqkaY4QAtNyWMxXeE4L12sUh3Vdv64IPDk7pkgzwnBFN2hTljmO55LmCaIqWc1W\n2LZNqmuois5gMMI0G8RAliWcnp7S6QRYjsv0fMyl/W3OzyYIIdAVC03V2do7QAjBdDonThIEdZPV\ntw1KWTIcdUmzCs00GuGL2sQdK5lj2ibT+ZhO4BGGIWWW0+v6hEXOyekRvhewXC8buYrVtKoURZCm\nGZWsqaVENTWM0qaQCpqhkZYFrQsLl6qqRPEav+Wys/s0eZTgGBY7+zt0Ox0UBHGWUik1rZbXIKmF\n4OqVA6qqYrFscM9xGGEYOkVV4gUdyixFMzSkIhi0+/T6Q+r6nNHGFpKKNMnRNZvNrRFqDUlWYKgw\nj9Z0el0c1cHselgvvEzl2/Q7XdI8wxAqBRVPPf4kWVnjP/1Gsjhhb3MXTWi4LR9DCkZ7BxctS4fN\n4Yjlckln2Afg0UcfZzwes7+/TxzHrJchq2TNM08/y5e+8EW8APRaJ85D1AjQBbPxmk5/hCogLVXy\nNAa9QkYhjuPhOC6aZjBbz6ktsNwWRq2g1hm+6xF4AbksKMuSjd1L1KogCVOetDR0XcfSWqAKTqeH\n/Mff/x4qx+bsdEoxW6DbNrlIUISJ7+7Sdnyi+Yyrb34LjmtTlhof/vAfok7P6BoGi9NTBj/0Tk6X\nC4TuMU8lUjU4ntY8dqkgu3qVkzv3aKc2riIpS4lBjTJuFl3Xpyfk41Pa1ERqyujN72T6uU/jzVzm\ndk4SnTFq+Sha48AQecoP/vW/yfb2M7x2/BpC9zl42GH3yia/949+Be36h1j3LX7rQ7e/42L95y3y\nvwP8VeAfXvz72992/KeEEP87zcbr8uJE8IfAfy+E6Fzc73uBv/Vnv0yj2ZvMpgR+QJ7HTPMFjmc3\nOrYsp5gvaXXajOcLXMsGUSOkgu91mnZK2UggbNvG85oc/c72NscnJ9iug3sxYHN2MkFRazw/4Pj0\nPv1+n/H0nIO9A5Iy5+T4Ho7rUiHpdvqsVgvm0wWtoIOuNqtrw/KbARrTIpdFM2FbpEzGKUIFy7Lw\nW21M2yKJYuIkIU5CBr0u4TJGqgq2Y7JcheiWyfHZKaZtNVl/PyCO0oaXP5nRbrdYr5txfmEoFNQI\nw8DWJEnS4IWXyyVJmpMXBavlkk63i241/fZO0G3EzrrSCE9sFxWVKFw12fE8BylRFPD9ANOySLKU\n3Z0t5osV59NzPCcgjCL2egOiOEbFIM/XTYGNY6aLOYPBBrdv38azLdqdASU507MpnU6POIsRsUqn\n0yM8PcGwPMIkJSsrVMPk6LBxsgohCJOUJE1JZYnrXCAqpCTwA8azKY7jYBsmdaXQ7QYNW0ZT8V2P\nxWyOYjWWKyklEkAIDNvi9PQc02qu6LLZGNt2qaqKfr//b1DDAjRVg1pimAp6rDQDcUWN6ppoFy5c\n2zQRtWTQ7VAhecMzT6EoClWWc+vGTbKiwULHYUxRFTzw4CMYeuPDXa/XrMOQJAyJwxWlzLl67VEK\nFG7ev4mtGeiWiWM3LbduzydezClkQa8bYJsqrikYz8ckcYYqavK0pCgqjuIE81bN7gPXqKqCvGwi\nyJ1eH8uyoCjR6gYqZrnN3sjly5cxHRtLtyjrmnK1Islyyrrm6kMPk6xDxuMzjo6OcByPJE9YRSGG\nqXFpeIksTnjmrc8Spzk3XnsNJVfIRImbF3zmkx/jHf/B92OZ6sWwT8MyopZEUYShGKDVRMsIMzWZ\nlysKWWGZKtPJHC0vELp2gft2Waxm2JqNrFVKTePxp3YocoU3P/0Ad597mZSKp559I1FecT4+pioV\n8qxEV2tiJD2vRZ6ELJcFiglPPTricPwavmUTnxdsjTb46lc+S7K6RazqGG6b0cBlESa09ZKk2+P6\nJz+FsAyEqbHh6gx297l2dYu3v+MJfK0CU6UQEl+zKN72KLVukKxCzDohVWvIBFVUcPP4Pl/5jd/g\nBed/Yb5osbJK5rMt3Og6ohVzlKjYUcVpffinVs5vv30n6ZrfoFmF94Ez4O8C/wr4TWAPuAf8kJRy\ndhGh/CfA99FEKH9CSvnVi+d5P/DfXTztz0kpf/XP+uK6/b780ff/GOtVhOs7JEmG5znUdc16vaYq\nahRNJVytGYz6OLbN0dFRA/0qJd1OQBTHGIbBfDHFdW2EVKilius7zOdzgiBACElepLT8LnmaEYYh\nuqmxmC158sknuXfvHkG/S52UJHWGqHIs20dUgihes72zw3Q8o9PrMp1OGW0OicNG/+f5DqZhs14t\nGA6HLJfNSvHsdExepDi2RxSu6Pf7gEIYL6Go2N2/wvl508svLsTXRV1h6QbXX7tJ0GlhXYi48zxn\ntVrQ6fRIkubqYr5YURYZoHBwsMt0OqeoJaKuKIqqaQ1dPL6uG2sUUkGoCoausg5D+t0BRVEw3N3g\n+M49DNPE0HVqqWCaGorSrJyKqqTIcoSQRGEjVTc1FdXQuXv3LnVdU+Yxsm42KaOoUTQG3ab3/CfM\neM/zKLOcvCop6opBv0uRXQio85zlctmkcrpdLMuiKCrW6yWu42A6LrZpUZY5nuOTljkKzcZ1nmZ4\nnsfZeIzj2FBDmjbawTIv6A77tDyfxQVjX5Q1bsuj1+tRFCWGZVKWJQKV9XrNeLkkS1LqssSyDNq9\nLqNuH11TmMwXSFlR1zWrVchyviBNosZYZhmskxQhFN7w1FNNQVM1UBVOT45J05TqYqo2jWIef8NT\nKJrNN5/7Cqam0mq18NsdLMcnTCJWF7MaZVnS7vS5cfN5ZospuqyYTMeoAlQEYQ6eruP+n9S9aaxt\nW3qe9cwxZj/n6tfuT3vPvVW3Olecwk6cil3EDjhERImICQqIRDRBQSSRDRERUaSYCCSXjRD5Ef4Q\nWZGAREhIdEVCYmywjV02hXFdc6vq1m1Of3a79mpnP0fDj7HuiYSIKIkEyvvv3nvtteda65vf+L73\nfd7hACkiJrMZB/MjWmNIwpw0c+C/XmusH1AWBWmaEkq3x+hUSxAHBH7CplqTBhmXV+fcOzulapzC\nKIgDrLJcX52DlQxGQ05OTlgul/R9y/vvfcDt4hpTFC5+czggTmZgGjQGT/TEcUrftBiVEgQ+xoPD\nyYxXy/c5PnxE23ekIkAJge4VTdfSNEu08vCIUb2mNz2xELTK1TXV1RweHqKUYdNvCGpDlA3xPIs2\nDY0ShL5LvsJ0bOoGXWwwvqBte1Iv4PRhgKcEq23JB998jB+6UPu2T3njE58nl4oii5mGAVXT8Qf+\n+/+Bnz8a0Hz6+2ibjq1osVYzGIwQugUr0b0BTzn6KjUHNmO1eEmpFix3MetaIa+fM3yQ8K33Y3xV\nYqNrPkXOh9Ed/tC/9if49//kj/3DGddYa//4P+BbP/J/87MW+Df+AY/zs8DP/j/9vf/LbzklQ6e4\neXrN4fyIy8tLRqMR6/WayfwAes0n3nqL69sFrdcTR6nrnqzLczXGcHV15ZacyiCkZDYbsl5tOZjN\nqduGtm44OTnh5fkrhPDJspSy3CF8yfn5OQ8fPuQ3v/kO82hIOMxYFgVKV0Sh4OT0lA8++hAUzA7m\nJEnCbrdzqhHfJw4jFjdLjk+P2FU12sCzJ88J44CD2dztEsZj/CCh2G2IwoxeVzx58uR1QZwMR4RJ\nzPnVpZNdDoccHx8T+D4vX77EWo1SHULAarMmbxLapiWOQ9brLR989Ji2bkD6jAc5vTI0e5JjU3fU\nbeW05AN3EjG9YjQcIqTBtIbLF5eMhlOE7xQ6TdPQtj1SGortjnzoaJtR6LtkIt0T7TNew9A5YtN8\nTNvW9EZjPLDWIw4Ei/WOIIjwfEcBtUpTVA1+HHKzWJNEIU1dMhiMSJKE2WyGZywekMYJQkBTtQyk\nQOue4XCMMU4Z0xTO1EQMQRRy//49lssFrXJa96P5IaPp1IWN94qjo2N2RUFVOq6PMoZ8TxY1Ys8t\n6loEHm3fYZQmGqYEe2z0pigpqgZUT9OW3NwsKMuSqiqZzyaoriHKcj73Pd9LXZdcX12Rpimz+Zy2\nbffveeOW8TLiZrEk8GOiKKBpKkRpiJIB81nC6vaaEIGnDcbgcnS9mMPpIVIpIhFTth1pFjLuIQzk\n/tEdVXO12hAkkqbYIOyUXdsihO8CTaIELxSU7ZbepmRxhupa6qJEyIhkFDBIM6quJ04imqYi8TNW\nq2vapqBXBj+wvPNbr5hO55yc3eGzQcD731Z88xvvIljQXQXESQM6oqxrLB1W+qRRSpymdJ0mjof8\n3f/2WyhG/OE/2nJ53hJYj03VIqUkjgavQ0nSxKFDIjz6zjDOYrcMDxJevnhFkkYc3T8l6y1XLy9Q\nWQLSI5ExMgCtOppWIpOEJMuRnnDZz1HCu1/7e/zI7/+9fPDsCY+fP+PRw7sQhrTNgnGmEUVJW9Xs\nthYpM77aK26Xa3xTUWUpAx0Qpe49ov0I1dRMp2MKo4lby6mQlP6Oz37+D3LYC/6VP/dn+Pys4Cf+\n0o/zX/yNX+Hz8Yp/7st/mX/nL/znfMv2HI5DRNN8x1X0uzr+Twg3Stisto4y2dSOXdK5DflmueLR\ng4evDUTlrkAIQZyE3Nxs8KVkOptxfX1NkiQorUkjZ0iK45jrqytX9A/mXFxcYbUhyRJCP0JmHhtd\n4IeSx4+fcvfoIeevnpL4OPxr2RIEGR9++CHzwwMmoylFuWa72/LwjTdRXc9ut+Ply3PSJOfFixdY\nD44Pj1B9z3g0oa5aOjRRELPbbGm7CryAQRIRypDVestwOGRTFrTrNVEU7MmLToWyaVvquubk5Ii+\nb7m6usICUZ6yWG+I45DZeELZOtNSFMcEQYApak5ODiiKguFwjFIhIvIo6oooiojDEE/4WOsRxQHG\nGJquYRgPkVIymYzolGaYZ5RFweXVFXGYcHF7g24NbdtyeXONVZosywgiH88K4jhmu12T50P6vnen\nq8GU5XLJeDogSWO2VcF8PmdZbBmmObu6xFinRY+iiKIoHIK4bUmyAN8POTwaYLuOeDCg73t86yEi\nQT4eInAMfBn4dFVDnmUcn0zwsEzHEzcmUJptVdJr5aijrTOPLbcb+k6/Pq34QoIUCHA7DOWQzuBC\nr8tdQRiG7Lqa9aag6zW9hbc/+1nOjk/YbjaMxmNWywWXl5dsVmtOzk7dSUcp6rrG8zySMGd255DR\neIgMYsI4QmOx0mecZ3hRzP03HnG72WCU5jAf0QvB4f03UW3H7WLBgzc+5yTGSIxhLxf2UNbJhj9e\nfnsh9AoOhRtlecLFGorAx297tvWOPB7Q6x6le6IoJo5jJxdOYozSSCnwhCCOIzzbU7UF1fkKiUff\nVaxuF3zuC1/gLb6HIPF59zd/kdXyPaIaRsczmr5kEILnCzq1ZrXcMM3ucvvK4Ec5ngxptaIN3yGQ\nAWnQ01HhJ+ke81EgrCSPhwgv4uDgIUl2jwd377GtGsJkSNvAxbPnDGOJkh1NY0gyJ8iwyp0S8yDF\n9A3BIKfpm/1obMsf/OEv4cUBj7/9EW+//RmCSBJYQRfHFHULI8GJd0SLQVWGv3F9w60a8H0H/wcn\nD/4xdu2K1VojZMTp6SlGBry6vEZKQZklnFc9ve3Y/NIvsHnj9/PHvnBAc+9P87z9ffy+f+kPsSgV\n7777AcvlJZ+ZhUzSGW27+Y7r6Hd1kdfG4Amf0XSCv7fYYyzL5ZJHD9/kydOP+Oa3v8lwOCSQIX4U\nsS02aNNzdnpMWTU8fvyYe/fuueUfsF6viYKQ0JecnB1zs1hS1yVpGrMrO4rdimR+RBAnDDyBUZbT\n02PeffddDg4OXFqQgCjxkdLj7t37LJcLyk3JwdEh1pRU5YaL8xuSKEVj8YRlMMywxuP88srJKz3B\ntthhrWa3XCM8jzSNwfNY7QqODg/JMtfJd6pH4NFoJ4fzJPh4PL+64uz01Llg+44ozTgaDXjy0WPm\n4xHX6yXjwRApwChLV3csbxaOj74Fg2F9u0RZw9HREWbdEooQYz2qzdZ1xIPUHVvzDKU6IKSparLR\ngLpuieKULBvQti1xlFKZLV7nkcUxddfT95quV/Rtg1IGP3RmozRNiZIREs9x97XA4lNsahiGDNOM\nqimJhWB4NEVpSxi4sUI+TEiTnPV2Q9PWWKuZzg8IpE+ehijdEfiRW4gnMdITLuhkPHW6e9grgkBr\ngxf5RNagi9IZpIyHtYbJdEDXtlg0YKn7HuH5+4WsIIhibGcgsLRCOUS0Zwili5trm4q2qjg7OyNP\nE5I45max5N13vo63T/6y1rKpCuq+587de3i+RPiSuu8QfsCurgjyGFXUBNLieyA9Q1W1xGGE8Q1W\neAhrXMEyPaPZGN31pHFKq3qyJHK5BNZi+o4gCsEPQIAQkmEaYTxDrxVWCaR0smAlJLMkJc4jRBuh\nlCFKA5qmI06H+HiEaUrTSrRS1H3P4ekjhk1D2RZcv7qiLWvadkv767/I4fw+n3j0eSQZ04sjXn74\nVZrtS2wbEA9PULql6DrSZIKVC6pOYwKf7XbD//Rzv8rDT1uIJG0zIIktlxcrkiylSUsCL2Gx+xbj\n3OM3L64Z+f8jP/LFv0o6maFNS1MYbm8veLksWV98k4+e/zqrZUV9u2bd7AjDnmGQgYkoGsGuSvjR\nL/0YdXPM7N4xzcUz7o5yyDsyoVkubjgef4JsNkAI6GuF9XpuLxo+8Y//ca6u3mFTvE98I3j0qe8n\niSRX6y0eiulRRhqFlNsSaTfslhuOT9/g+csLnvydL8MX/xrB7oq/+5Wv8jv+6A/T22s+/CXND019\nroKMVTvl6W988B3X0e/qIi88D98T1G2Hn0bUVUWcJhyfHLK4vSZLByijaaqWdJ5SFTUHB0csFgs2\n24LDwzlKKbbrHb3tiaMAKT12ux15lvDhh+8xnR1hNfiRIAxj2DNHqt2W+fyQ1fqGzXZFlif4YeD0\n51lOXXeMhxnnlxe0qmX23O+3AAAgAElEQVQwHHN+/pLpdMr773/I8fExWhkGgwG7zZZUxxhPcDCb\nc3O7eK3bHoxyh9/VGunHVJVzv6q+5aOPnnN0dEK5KziYz4niGK01fd9jpOVzn/00T188p2/d4i7N\nYoaDzMkm45ig2DIeD1ksHB8myxxpc3F1CQdzhHVjjDBw0LayqvGkMzR9/Nz6VjGcjdDWtYNN0zCf\nztgWO+q6JgxD+r53kswgpCkbuk7RNCvM3j1c1gVJFDOapNzcXDGZJAgZoY3h9vqa09NTuk45DMDh\nMVVVMR6PKYs1QvhYXEG2aIztqAoIREy5q5yvYLMlSTLygwMAsmjg5IGAlD5B5I7KvvWQeK5T3S/7\nQumjcbF/tVftTVM+2hgWi+Xrk4tWygHjfEkY+hR9iyclVhtaregrRaUUkfRI8sHrEJrhcMjt5QJx\nOGO92vLk2WMQbveB73Nw4kLST+/dRVtLFPgs12tmuTO+FasNZePGVXHk0xpFIjN6Y2nbeu+69fn2\ne98mH4zwPI/WdMjQo21bkjxzPCJfopTicDLGKk2YpE6FFUiE8Ehk4gJuPAMiI/YD1sUOb0/mlFKi\ntSUOQ7pWoUyP7d34U3q+yzvWQ7pdSZDGNNWW8fGR87bYAuuN0AI+ePoBX/jC95I/O+LTb/wAz26e\n8867X+NqdYGvFQQRsfDxZMz2dgm2IQ0lkRwwmSe0lWKQleDFpMkAI3JmdUUfCpSMuHf2T/G70k/x\nlf/qv+Rf/lP/Nm98ckU286jbD6hkiF7AYKYwIiHOGsRgwHimGCQxgb4l8FO0lUwO4Vsf/FUaL+Yb\nH9RkgxOmv2PEfPSMrgyZnNaE8Tme/y1gSKd6lsVj7OT76J58hbOjY6z1aLv/nfff+yVsPySK5xx/\n6kt4tSS2d/CyAEXAh+/9Cn/sD/9JLu4fk7Zf4ie+8h/zubd+jA+TgkfbBl2suXP7n/A7//UfZyfc\nNb15/ovfcR39rk6Gmsym9vt/8AeZHh8ihGC1uCUdpAhlkGGEZ5zeNclSNssVSRYThiFN7QxTaEWn\neoqyJktyZrMJ682SJEl4dX7JZJxTlC2q6zm9e8R6tWU4mVJuN24hq30mB2OePPkIGST0zZZHb36C\npy9ecufOHbZbJxVcr9eEAH6Ah2G1LcnzGKyl79zIAuExyHLKzRYR+KRZRpqmXJyfo61yC7HJhLqt\n8CyMJ0N2my3GEyRRjBSA8NisdxwcHNF3DV3fE4YhVV2QpxlFseXqesnh8QFSBnR1w3a3I44kVeNM\nSpPRmOVy7cYycbzPlG2pG4Xvh6RRiJCSEEGhenzhRjZhEDMez6jrynWp3cfIg4b1ek2eJdyuFlit\nWK1WKKVoiwbhS/KRkyNaa4njkLJumE6nhGmEsIJkMMZXzllrjMEPA9q6cVTBKKUoCuaHMzYbl//a\ndZogDPcNgCNIDvIRo3FK6O8NTV1Hvs/RBYemttaiux7ru5FEFLhuX2v1Ok+33BUIC7fbNXk+YLct\nScdDPG2wwtK0/R5p3bndC8I5pvdZsMPUva7vP/6Iy5fPODo8YDaboY2H50t836frGrS29FYxHo8Z\nJxnbqkabniCKqGs3lpyNZwzyhGWxIw+dtyMME776v/wyP/pP/hPgCW63a9q6duRKA02vEHiuMPse\nnhBIXDKZ9ARSQZjE+EGA8Rwm2fd9hLbI0N0YQ7kPZen612a1Xil3WvMDemuou5YkjBimGXXXcnV1\nwYMHb9B3mrbvHJJCK6pmhxZgu96FtShDW9d88hOf4vz5C16tr2j6Bs/zefHsMV/6kR/m+de+wc//\nwn/GrpzjB1tkEpGrId//I/cIzY7NxqICRRK1mL5FRvf5U3/kz/IX/vK/xZMXL3jzMy07pTmeWW68\nb+JtIZdQmAiRBmSyo+oD/M6n0yV5Kuh1QKVKDoI7NLsV4ahBC4FuDeMsYSwsnYTAz3n5yjIaztFi\njVQjZNRj/ZRlsSVuUoK8JIzXrJf3iabnmM0n8eI1udphoiEq7PF1AhxT90/p5Eu8bowqEqrxGd++\n/Bc51JJnH77iRz8/42uX3+Kf/z2f4T/62f+aH//z/yr/3l//S/z5H/1z/Nm/+Kf/kerk/z/50lqT\nZhmb5QrpObYJe2RslPp41kf2LiA7GiTUVc1sesTy9hmDgWU0mfH0yQecHp8gw4jL6ytubq6YT+bu\nsXyJVh3D4YjFYoEvAvqy5nB6zNWtc6SGhWA+nxMEEdutz25XYnrF7fUNUko2yxWb3ZpBnjMeumXj\nw3tn9FpRVg1ZGrjln/Co+hZCH+H7rMsdZV1xcHBAp1rKon0N4Cp3BReXlxxMD7i8ukKOPPI8Rxs3\n746igCj0Wa3XTpWSJRgr0cbn4YMHrNdLFsXC8c49EEGCbHsOJwecX10zn0/JMseyNsYS+gmN2RJH\nEiHd0XunFPlgwGAwYrNbY/C4vrmhbWryPKcsdoyGA3xf0HU9wWhMU/YU5Zo0dUakPu3Z7LZUTY0v\nJEEQsNvtSMKIuqzwhcMMXL14gVKG8XjsftcTDMcjttstchyQJAlV1RDHKX4Y4XkdBk0Yh6x3HZPx\nmE61WOuUJuCKuhDitVsV3PsG4WE6te94GzcCBLfstBDGkVNxxCnSD0lTi1aKLE6IQh/Pc5GFbd3h\nKUPZ1sQ2eb2czTwwwo2gqsGALElJsyGe71EU1X6k4zmaaJ6QpilqH+YSCulIn7uCaJCxqQqW21s2\nmw227wAwGt54dJ8Xr57TakuUJMRh4iIrPQ/ruX9I4DmEwh6m9vHfbfve7Rj6njgIXSRmkriF8j5K\n0fd9PM8jjyOMCRBCsKsb9H6Rzn5saIxhV1f7G+qQ6+tL8tzp3EejAVpr8jzndr1i13YUZUnXOdTH\nL/7K/8wP/cAXOTo94qu/+TVOTg5pyx3tpmB1+xFGpaSJxSNB7RqKoOb8RcPdk88ym3UUyyU/9mP/\nAn/x3/036Yqv8413fpyjt1/xe9+6YbUTJNNbzj+0nM0e0STXeEHJtI9ZmS22S/BtRZSHWBM5OJ1O\nicqAfv4SFYToVhNFmiyC9a7iyhikgsGkRYwlDRuKoiWQ50SM6XYNYWCxyYhdt6KsxuTpC7qtYKuf\nkxQb6n7CNDDsVlsyWaF0y2h2QFUojLpD79dMvYBf+9++wnQ14Hu/T3D4e76fw6/U/MaL9/mn/9l3\n+Cv/zZ/hn/nBv8Lfe/kT33Ed/a5GDf/Ul7/8k2cPH+B7wnUmQoA1pHFOa1q6pqNpGqSA6XSO0Yqu\n7QgjyXK5QCsNnmW93jKZuLlxEAS88cab5NmQoi73VndN27RkWUYYhaw3S2azA6pixWg8ZTTMee+9\n93n0xgO6pnMo28DHGM3R8SHD0QgpQCmNUoqiLBmPppTlFm0UURS6o77GhVY0LUopBllO33X0qn8t\nR6zqkqZuMNoShCFeEFKsVjx4+BBP+kzHI54+fUqeZQyGQ+q6xpc+290WZRusNixXG07PTunbDmMt\nge+TxAm7siBOY3bbLXXTcrtcMhmPKcsdSmlOTk5pmo5iVzIcjcEaPARpnBEFETeLBQ/feIiH2RNA\nG26XS9q2J9ifCLTVFGVJFEcsFkvmsyla9YxGU4T0qXcFdx88oO0UWT6k1ZrJeErb9PuM1Qxj3fgk\nCH3i2DmBh4MBfuBjcQHcWNDW0jZ/vzjFcYb0HAYiSRK3v/A8J3drW3qt9ktOjbWGvuuJ9mY2rQ2e\ncD/r4fZBke+jjCJNYoLAhXkbPITn0RvjjGddh9LKNSR59vpmdn51znQ25+jkmHw0YLstMNbuuUuS\nMAqI4tjxiXxBrw1a9VSVUwSdHB4RBxGDLCUOIvqmp2xqPKMYj6cY456zUQoZ+hRlyWqz4Xax4Mmz\nZxRVxXq7dQlWSrnnrDVpGDGIUwZJShTHJGFE33X4QYDEdf6B9F+fvDzPyVCTKCRNEsLAx5eCKI5A\na4I4cCeh/WvQNM1rJlCaJBjttO9NUZJmCU+fPGU+n6Gs4fzmnOPDY+7dfcDF4gJPg2oa6mXBy5sP\n6Y2H1ZCmPp1n+J2f+yJf+tIP8clPPuTV9S3ffvbz3Lvrc++hz52HilE6odPXEG1I/Jwwg8C7QiO5\nuTWkeYSRPkMvJBknWFMTNC29D8NAQRRCZpGmZ5JLRCC5WBryNCKPDY8eZdzcNswGKR2GNAu5ve1R\nHhipEaFhVe6wCIYItCmxMiMYLMEP8RJLrTS17sjznoIFvV1hwwrVnYNIaKtv84335vD0V3jv6Yaj\n6YzV45+jaJ8yG6x5ezgA7+8wHfr82i+Wv/158j/zH/zMT957+IiD0zNQhuVuS+D7pHmK3Fv7ZRAS\nRh67bUVd1cyPDuk7xVtvvcnFxTnD4ZBhPuT588dYIzk7PcJ6sHj1imyYIjzJZrXl7M4pwZ7tDgbf\n9wmDFKzHe++9z9nZCYN8hB8GLFYrsihF+pLVcolWCqMlYRxweXFBOsxYXF+jtSbwI66vbwj8EK0d\nFfPevYcM8oxeKw7mB7R9TxJHvLp4hbUe+SDHwwPhIYUkzxOE9BkNB6xWbj+w21WkSczTp0+p6gLh\ngSckZVFwducu6/UtShtmsxkXl5ekaUqaDzh/+ZIgiAgDSd+5WXrXK2Qg0L1Lg0rThK7p8IMQbXrW\n6yXCF9y5c8p2WxBFMWGYYKylriveeOMhTdMiPYmxirZuGAxGVFWF1s72fnbnDjIQnJze4+Z2wcHc\nAcU8q+mMwRcex8fHDCZjxuORG+2kOePZBCsFVevwyq7zTGnbju12h1LKzd+lz3iY4wmPLM32nbzE\nGENZ1xjrumVr7etuUkgfzxO0bbtn9kvatqXdq5akL0F75IMUrTSN2kPJpETvwWVl29A2DWHoE8Ux\nvh8Rh5LAD2iahl73CODlk6e8fPKSy8tXnJ9fcn11TRQEpJnr5pu6wmL3BjSP1WbF+auXdF2PwVJV\nFWEUMZ8fkKQpni+Rvk+cxBgP9z8bjyhJSXMXKehZj75TGAzGelgLCk3VNNR9R11XVG2DlALfE2zL\nArSLifSFh8VD709BH98wpe+aka7v6bVis17Tdi1Vr9jVNb3SGJyMVmMYZjmDPGMyHXKxuObk5JjF\nYsVsOiHyY37rG+/y6MED6qpD+vD1r/4q1zdfY9sIfM/iEaMlhJFHLD2efvSETiW89+ycOHiMCBQy\n9CjKNaFfouKWxdMrZBrTFBEXt2vCdE4eWhjUpJ67mV63BV1rCQdT7p0NaeodgyMfXXRQgxh4RFpy\n/yxiOgGjJFfXFeMYlpuOm7VPXXvoticfZ2w2FUaEaBNy3Xb4ScvjlcCKmsW1ZhQn7PQCIy2RXfFy\nV2FzydVyhwxbMFPa9pJdNuKj699N0Uvunc75+q/+bZKzgKvbj/CTSwaJRxN39KrgN35Z//Yv8j/1\n5Z/+ybc//RkW15fEQcTNeolVCg9B3dZIGbDblQ5Mti3QxjAcDGmNYnm7ojOKPE3p6xoZxUjf5S02\nVYvwBb11fJd7d++zuL1htVo5AqHxyNKIsqoAw/17D9hud/SqZbfdoJVC9x3GGtI0Zbtdo7Xaz1s7\notB1h3mW0fUtYBkMcyyG6WTO1dU1YeBOJU3b8er8HKMNDx4+JM8H5HnG8naFJzw8zyCEz7Nnz8jz\nITc318RRQtt2xFHIZDrFGI9OtaRpzKtXl3S9Q/5KT7JZrxBSMIhTnr98yqM3HqG1oe8VSnUcHc3x\nZYDSPbvtjjhO8TxDlmf4gc9kMkN6Ek9I+l5RFiVhGHJ5eclgMNgX2w3Pnz53J5GqQClDXVUkcczh\n0RHGE4RRTBjGDPdB20ka40ufOIzx/YDT01PmBwfUVQVYxiOnEEmSBIx11E1r0MbF6llrUUaRxK5j\n/+Rbbzl+fhxjjEFr/Vqa2PX9HtSl6ZV7nTBgrCEI/P3P6tdjHa01xlq6tmU0GdE0DU3dE4Xh6yVk\n0zVUVU1ZFRhlSIfDPcDMI41DBsMhSeqwBUIKrPCYnx5xcHrMcJATRgGT6YQscw3BcDJhU1QUZUkQ\nh8jQPQbCyROzLCWIIqbjGbuiJMkH5FmKAcdLshZt3I2sbRo6o2j7zi1Gpdh3/hqtHD201xpPW3zp\nEwU+qlcojKOQhiFd272+Hh5uSa33nCLP8wh9nyByxFXpO7ntx6eYj28auu8xvSbLM8IgxBceZVnh\n+4LL6wuEkMRRyIunz/n6r/8WiecRqJRXl+9h5QRDg7RDotRDqgGjUUKeDhkeDrm82nFysiAZ+YyT\nlFGqUPEatW2pxZSurpBmzMPjOfloRdfuCI0mkhoZ+CQJzE9npGJJLHtGh4dI0RMbxZ3DucupCDWi\n07TCjb68wPLq1lCXkGDIpGA8HbEzPV7SsbhRHE8jYtvBGjxtmGQzF4AjPRZXkqtnMaQSv+upC0MU\npmwry2a7Y5ymeL3hpfcmv/uLQ5Kxzx/4/sf064Y7b9/gJydUdUO6VtQy551f+85CQ76ri/xP/8xP\n/+Tk8Ih8OCTJUsI0ZjoZIv2AOBKEMuT07IiXz1+Qpgnr7Zo4jljd3hJFIVZDuSuxwn0Qtpslq+Ut\no/GUpm5p6oq26VmtNsxm7gPneR6zw0NU01J1YLqG66tL3nz0gLJ2aoYg8pFSEAQhRjvd/MdzzDAM\nKauGumsIg4B4v+C8vb1lV1RMZ1PSLMYYaHpXOMaTCbvtlr5v6duOwSBluy1Is5yu1QxHA4QnqOuG\nPB8QRSGj0ZjNZs14PKZTHb7wGSQ5V7cLTo5OuDi/cGqb0QBPWtrOuXSlL1D7/NEgCBFeSBwEyNDH\naEsUR4RBiDWuk1OqQ/WKg8MD2rZlsbh1WntrkfsC0veK6WxGnCTUTePGFn7IYDxkkA85PjpmPptz\neHjCrikZZQOiKOXw6JDAj5nOJhgPlqsVgZBMphOaukEbvQ/q6ED6bibsB+he0Xadkw56rugiPeIo\ndp3nvmt344MaPM/p5wPXXVtwiGXpoc3HC+GEruv2fKHaIQTixLGLfEno+yht3DzbuPFPq3u2m4oo\njAj2WnFrIY4CJpMRq/UaK+B2vXajJm3oOkPoBwyGGRhLVZa8ePGSzWpFGoXkeU4cZ/TKZcNGcch8\nNsd4MB6NqZqa6XxKaB0mY5jndF2LJwSdUmjl/m/rC9IkoVM9VmtgH7iChzLa7Xe6DiPAsw7AFkUx\njVJYben6FiOg7TqkkDSqRxlDp9Tr62uUJh8OCPzAYZ2VwXjQVdXr0U+/d6c7mXNAZxQYweHhAU1R\nEUifLBsQxIKLp89J0hUvbwqs6fFtTBgJygqOpgmPPvE2Rhvu3X3AbrXgzslTmrLCs1d0WrJZSVrt\nk/o1Ak1nP0JlkrZIidIKE0Vst4rRRDMSAQGKQGo6pQj6kMfvbvjs20Oe7pbkIYyzkKuNIpGa1aJj\nNo2QXkhrFSYbsE59bm5qYuEajdPZiDUlqrdceCmzcc+i9+hsR6otu6Cn6Wu00Exzy8ZaBnmPxcez\nks1ViZQj5v4v8/hvP+W9X3gHOdkQ25TH1xuqi5zrQtL3ITdly7Nvtv9wkqH+//xSWjMcpUhfsFwu\nSNOUF6+uGGQZfe+swm1bkw5yjPW4c+ceWmvXWWiYTscYrQn9gLZTHB2ecX5+vl+2Dmm7kiRx3dbV\n5Q35ICWNM14+fszh0QnC1CA85ocHPHn2nK7rmM3nvDp/wWQ8YzBI6DvNdDbm5YtzlutrJtMBUegC\nLhaLBZ/4xNssF7dk+RCrDevlCmstp3dO8FtB13TsioLxeIzwJev1lrtBzHg6QfeKs+NjPnz8EZ/5\nzGdAWC5eXfLmG494/vIFeZ4jheD68oo8y7jtOj7z9qcoii0HhxOqTeEokUjG4xHb7Zrt2nHf1X4+\nneU562KHEII0y1CdJook5XZHOnJSwF4rtusNTdty9+4pRVGTxi6EpWwbMuHSouLYMffP7t4hyXK6\nukNKj8l47OBddcHRbE4YON22Z0F5Ck9ZTOcWgrpXrJdrhLAY3709g8jhoIMgcIvKrmNzWTDc7yQm\n4zHDvZLGdIq2a18vULW19G2L1VCVJUL4GANlUxNHIWka0iuF1W5BqbQmzlKGWe6w0FGA6t0187Si\nbMrXpqG+6ZGeRYaSII72y1c3n18ul4zH4/3ewI0Ad7uN8yhUJb7vs7i+cSaovsMPA15d3dDWJUcn\nJ8RxzHKxYDAYsGpuneqmLJmMRqRxQjKKQArkfnFqjAHh0bYNRdNQVY7jr9oOE/jIYO8NkIJ6uyVI\nY8CgmhplIcwkXVuj2wbixDl6RYDvOV+Dwb72BxRNSxxF7ibcdqRRzGQ44fzqkn67RRvojMckjGgB\n0WhuFwuGwyGzwYQ2VVwvbji7e5+iKmmqklhq4mjE+8/+V2Sb4mcpfV+6sBo6stGI569uCK3lmx98\nm6prwZNgNZtyjOd5DCZ3ePns5zgcWLJ4hLe9ZTo740X9mDAYM8lDDjMQ7Lha7UjjEX7d0npguyX3\nPwlPX224czTl1cslRnXcOz3g6fkNdQHFc4UdSsJkyvXLLcNYYXxLIFu60lLEa6gs2fCAm/du3D7B\n8wh1xqItyQc5g2GPpeVqC6NZyu1FRRdIaBVaRTTnFxzfOyIYl3zfH1GcDAYIu+JhdkZdlHRR4fZE\ns/g7rqPf1RLK8XRqf9cXf4BWm9fH5MlsTFM2SD/k8vqC0WjEwcHMGW18nxcvXpBnEdvtltOz+y6B\nBo2QLh1qs9kghAv3cEUjpteKumxo6o7D4wOiIObq+gIhBHk2ZL3dMD2YMxuOOb+8wBj1eiTgS8ls\nNqPrOpI0R3U91nS0vWKzW3MwPWY+m/Hq/Jwoijg8PKQsSzwJddmA8IiCkNFwiNKWqirQ2pLmGQ/u\n3efl8xccn55grWW9XmOMcR9qpem1+7B88q1PID3Bb77zdaR04K5X5+duGRlFHBzMaMoK4wtG+eA1\ns6eua9brLZ/59OdYLxckSYSVIVEUMcwH1G1D13UEMkBbJwUty5IwcF1v09WUTU0WhJzeOcMIjySK\nwLqusaqafbyhYJxnzA6cDHK72SGFB3i0WhEISde29HtljDWa4dhhDFTToT3LOB+612M4oKkanj5/\nth/X5BwdHJKkkevurcVq3Bzd6r25yXB+cYXnOZlgljntuFXa6cjx3KnAOHiZ04S7BayQEl9K2q5j\nV5UEUehuRJsdVVFzc3NDNsiJUtfNOW6+RPoedVlRbndUZclytSJKE4fNvr5GxnvVlTbMDg6pqoq6\nbjk6mLHZ7RiORwg84tRhMiaTCaEfMBwOKbZbiqJwMlTfqY/8/Q3xY8MgsHf6OiWN1nqPoVYMUqfc\nqaoC6Qmnca9aPN91+XK/p0hCh5HoVUtZlq+LvN2H2XieRxKERFEAnqVTlq5VFNUOKUAIH4XG70F5\nTiL7ceiLNoaqbVwCWVtz8/TbKCP41V//BaROGAxjROBTV+509dbDB0yP7hH7lsDPqPua4eDnUCJm\nubpimJ7R9ZZl9Q6pnSGDhviwQ1UBk8Fb+P6C7XLDqvoNovAN8BZEYUomSnbrHTb1MG2In4dsrnaE\nAl5eQJCDshG365Yw9TCtdSE/mc9BElE2LeFUoJqOyyUsLyWTXJNNxvS95MntLaepxOs0vo0RYw9P\nNTxZWN68NyTCsKkVfW0wYYDSFfQ52rd0BWxNge/DJAjxY01VGPJE4qmY/+6vF7/9JZR4MDk64Op2\nSSQFWgmuFrccjCYg4XB+RDbOub6+Igjcm308HpLnOVbjZGNSoPqeo7NTVovb19bxIJBgI+I84+rJ\nEwLpE8YRi8WCMIzdCEZapO9xdu+M29tbDiZjfF9gVUCYRFxeX3B8dMRuW5PlES9ePMMaw8nxGUZY\nhmMXQG6MIY5jzs7O2O4/oHXbMZ9O0FpzMJ9TNjXDbEQUBUynB9zc3nB5ecmdO3foVct6V3B9fcNs\nNma5XJIPhu40Ujfc3C5o62aftCSQQUgQJiRZwtWLFzw+f8YwzAiTFKU6tLacnp5ycXFBHMRcnj9n\nOJniBRFd2yIDF703PzzAWvem7nqzRz87qqexys2CjeHO2RnJaIhnLGmaAlBVDU1dcnp8zHZXU9cV\nL168oK5rAukjwojdbkuyT4Ta7srXEDZhDXkPO9GSB47u2FqD6RXF1TWB7yGjkEBmeKYnToLXN926\na7m+vMBHkuQDWtUi8YjzAR4QpxGxdLp2L3R5wartwHoEYch2uyUIAkajEdvdDnA1U2vtKJeAVpok\njtF4pN2AJE0RFuI8JwpCrA9R4NPJlvnRIdc3K0bGsKoK6r4jSGKc7UGy6xvU1RXWgyRKef7yJaHv\n0/YdZyenDhjnRwQyRO8NY9aT1L1y0su+IxsNnZLGWvADsiCg7zuCIGa93nJ94Wiefe8Wz77nHLmm\n6xmOx9yslhzOj9hut2irAA8fj7qqWAlBpxXCaHctjFviIgWR7667tZogiPB9gRA+IshRXYdF4ssA\nKSzWKFfYa4e7xloWi2uQPrEfEGcJTeeR54e025bt8haTpQyTAUJ1BJ6gWd7QWA0iZnA0QQcTujph\nOoupd2uCYUMYzri9LZjMYyZlhokTFqsPGcU+stN84ZN/gq9++LeQfe9GgWpHGcBb6Yibck191ZLm\nIeuNTzyqaLYxu9AwvDsklluqCxgchvRxB71PqkesdUJZvkRFAfcPenZIhC5JwzOOjg3ebU2lBaMJ\ndGVHcEdybBVy07PpNeelIRprsrZj44GMdoy1Rzix+DsfoxTGCrptx2CYEe1KkP13XEa/q2fyP/XT\nX/5JhSGWLpoty2Ks52GUdlAuo8FaDmZHuAbGEEcRYRDTdj2DQYrwQHWa7WZJ29R0XcvR0SFt22Ot\nYrNagfUIwwAL3FxeMRgOiKLoNUe9a3ussVycvyJJEjwpsNZwdnzCarXh8PiQ5WrJcDAg9CNm8wlx\nGpNGMevVhuFgQJw63oofBkxGY4S09KojjJx9f5ANXQqQCLDWAcKur69Yr1d4wqepGx48uE/XdMwP\nDmjbhs1mQ9c3RIdQ16kAACAASURBVH5E3dQ0bcN4MqXYlWitOX/1is989tPEfshoPEWplgf332Cx\nuKZpewJfom1PlGQEgWS7KxkMBoRBgNKazWbDeDzG4JHEfz9opOtbRqMRTdsThzGHJ2cIz7ouDUPT\nNlRlgR/6tE3Dcr1A9T1NXWN0D3ivw02iINxruC1GWzBuMZpmCeMsd3N/IRBYoiAgCkMMLlA9API0\np6gqEGCN5eXzF0RhQhAG+IFgOp6QJAlWgB8IsjCm73sXq7hX2wR7NLLneXudeIjFyR2FcNmMTeMQ\nyEJIEGLf0RqSKEQZ+1rmGyQRke8TBCFREtH0HY1yTtnBaMhwMOLo6Bg/CFFKMxmNCOOILEk5ODqg\naRu3g4pitDVs1hse3H/IdrsFazHW0vctSRzj4ZElCRKPbVGSZjmBL9xSVUrqqmaQ5QwnQ9TeJLXd\n7dzJdc+B8qygKCuUUkxmUy5evSIJI4qqQPqSpqnpu5aibun7jt4YlFZgNJ6Avu9Q2oLnIRF4e9ib\nJ6UTIwiBFSCFyxc2WtEZRa8M5XqLlHDx4gLZFvynf/M/5P69HyAeCKwBqaE3PSKA+3fehDgjSFN2\nTUvkx2w3HxBnklYtifNPEvkThsGQ1F8S9SUGQRyNmA8Vy+VTNt2HdP0LJtkdoqCiFZZh25OHASYQ\naG9EUVTsWs23n/ckg5BWtJzOU4qqZCxGnN0R5L4iL0PO64Z1VdOutmQeTLOA0k4JZYuyHkcHhr5o\nIVJoryfPFX6W0Dc+QXKISRW3RYuJU+6YlCebhok/wGskVvREXoYfCnalwhpFEgqKdcfk5IBlteXx\n1/mOZvLiH3Gd/n/1ZbTm4OAAhGV+MCWKY2ajIWWxdak8Uxd28eTZY5q2omk6yn0sXhg5pcuL56/Y\nbde0nVOpzGYzttvCvcExhGHM0dERWerMN5/9ns85TbbvUzcNFxcXdF3DbrcjjCKH8FUKISVlXRHH\nMYvrG4d03VXUXcv5xRW+5xNFCW+8+RYaz9ESm4YwDFnc3iJFxKfe/hzGGM5O7rij8N7I0jQNT549\n5fDwmLZT7u+kCYvFNUW1Y3Fz9drNKYSgV+3roI+6rOm7juVyyXQ+4/TuPVabNVkaIwl5/4OPEDJA\neI7waa0zDVVNvXdUum42TT/u+jW7zZa+7dyi2VrniA1DBoOMR28+pDctvpRUdUlZ1xSFu74Cj6Zy\nxaTp3AmhqEqKsmGzLyxVVbFcLumModov+3zfR/oOLwyw2WzAE7Rdz83NgvOX52y2a1abFeX+eWtl\naJqO2XzKwfEB6SBHBiFV21B2DV3T0jaKbenShnZVid3nziqlCHzfOZR7972PlTm9Vk4ia9w4UFjQ\nnRt/eL5EK7tfuFoXho57/k1b0dQd5v+k7k2CLU3z+6zn/eb5zOdOeTOzMrOGrLFbak1GEhJCmAiE\nRdhewIog2BLaEkSwaEBIMsYiYKMIZBxhIpCEwxgsAmFjDUhGoyWqq6qrqyqrcr7zmb95flm8pwsH\nC6sXLFq7zHtvZEbem+c97/f//37P08NkNCbPc+qyoWkq2r5hl+y9CJZFnqoZfRzHX/LzZwdziizH\nNE0++eRjDEPDDhxenr9gud4oxeR4RJqmhJ6vuOrbNavNmvV2w3K9YrlZs1gs1PdlPMZxLWS7f/P2\nw/1ORvUQTNMk3yW4bsg3vvE+H3zwIU+ePWM0mWC7LrZh0gl1k0/zDKHrLLcb6rLicnFDHG9Jipx2\nv3z9Niunrmt0XUeTYAgN17IJPB/Rd7x48QJT9gShy9//9V8k8AVHJzaakGh2yORwRNcJtF6Dvmdo\nGQT7N4vI1xiPwA8M5qPXCW2JbXS0VUxgDRn7Jwg7Iav+N5ZJg3d4l7t338IwBecXn+FSMqxKWhcq\nved613J9vuazGqRlc2su0HwYzzxMv2R+4NKaW87Pcz4+b/mzTYmMIRxaHB7r2CZoniRZLtheNvRO\nzbPFDt2t8IbQ9+D2LsVasrzMWC2X7NYJruEwFAlJsyEybbIuIYwqIgN0LefmqsDVYBAE7NY9owFs\nswTZhN/xOfpdfcgLTQNpEPoenmMj+544SbA9F9tx2KRbknTDK3fuKmtSliOahrPnz5C9oG8VVvXw\n6ATf9SjyCsv0SDPFmM5zNWNM4kw5Yg8OVLa5KinbioPRSHHee50wcum7jqPDOaBRFyXLxZbleoUX\nDehki2HpDKMBo2iG4zgMh0OevnhK3za0TYUQOqZucDSboukGXddzMJ2qBISUXC+uOD0+QZc9UTDg\ni+eP8YI9zChLiJOtAp2Jnk26Va7ZuiTJYjpdstquyIuCrMgIo4Asyfg/f/t3GA8HXF1dcefWKYPI\n42A+R9MMBoMRURQRhAMs08HzHHbbtWKUNxXj8ZisyBkejNF1HUu3qYoW11W7jdlkTlk1aFL9Nyrr\nBq0HXTPw/IB1vOP6+pquaimLiizN0TCwLYPD2Xw/G1e33yyPEbLF0pQMwnOVTk/KnrzMWd6s2KSx\n4sTrOqbtEI3HGLaJaRiMhkOODudEQYguO+q6om4VCM0yHdKsAClZb3bkRYXQlJB9s9mwXC6VKKXc\n1/hFT1rsgWq6anxqpoGmKeG6aerYto0hNHzXxtB1OqHwBgYC0bV4jksYeBxOJwhh4o9GdLJhNBnR\nNCqn33UtRZ1x994rmLbFyckJolPN3zovcCxLYX9NnV0Ss91uCcOQKFClr81mw3Q8YZsWtH1D3XZo\nmqAuGwzd2duqTKq8oGwbyrrl9NYtpICXVxdfqiRplUrS8x3avuEn/tW/zKuvvkoUDXn57CVXVzfk\nVYmGio52Tc9itabqW3ZZqgBdEtIi3z8R6Qih03byyzJa03eUTU3Td8RpQlYWnD64qzSTpkNZgS59\n/uH//PNE4RQhCrIqQ/Y6WtFQVjtW22tmkzmGOCZwLMZDHduAumoR/edMo5TAXnJ0MKG2PuZw+jXu\nzf4aqSkxeg3HO8Eq32YYjekNya7qSOqQLHdpMklndBz0BnVtkzsSQU1eFmxjSVcIAgN8CxwJhzr4\nY0G3q3n6aUdjgC8Fs9cGdCH0pckwGpAtOm6eNWi6xecvJNaRhQkMDwyM1kOaNZ9pkPdgXVZMxJx4\nO6PqPKxgztyPyCt4tkkZjAN2W5XTz2/y7/gc/a4e1/zcz/3c119/6002u5i+a5ESxoMxbdNi6IYS\nB5QNRaluj24UYDgWURSxXq1xPU+hbk2TXrbYjoWkx/dcrq6umM4U5tZ2XNI4xrZs0iTl4vqasRfx\n5PIc2fV0bYdpG2Spun06toVpmVimhWUq1vfRwSHHR7fwHB/D0tnFMev1hpP5MYNogBA6eZ4ymkx5\ncP91LNtks1oRDQekSUpVVQxGQ26WS7a7HbZr49i28mbWDWEUsY1TLNPBMCw0KSnyHNu0sF0XXTOx\nTYeiKCnyHF1jjxcQNE1HGISst0smkwlt23F8PMcwDC4uLnBc1cg9PjrBMk3iJGYQRWRZRuj7yF7p\nAoWmYZkalmMzCIagS3xfFXnSNFH5aN2g75Xgmb6nKiuqtlUYCcA0DCqpyJRto8ZgVVViCJPAD7As\nB0PXqLqWvCz3oxmVGR9GEffu3iUKQ05PTkiSGM91CcIAEGw2G3p6srLBsAzqsqSqKmSvnj7KIsPY\nt2ibuibLcvwgpKgqslI9AZZFjhRg6CZJliJ7tYBtun17VqoI5H63iZCSqm6QfUePigxKqVq3pmmp\nTkKnfLDDMKIoS4z9LNt1HP6lH/ghzs7OCaOIR198zsmtE1V4CwOkEJi2yXg4Ik8zTm7dUigKKRkO\nBjimSde26mlytcL3XLa7HU1dIzRBmigHbtd1uL6S5FiGwdHBIZ7tkBa5ak1XpfLlVpWioO7WdLJH\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kh6arKKNAp+4lH33yiNM7t9CFga6bvPH2W8iuI82LL+NvjuPsD6oNp6d3aGUPbc1yvaKt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\nuLaBrjXlrtlqRVGVqFXjNTsajWi3ukznC0RdMB2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1r6DUEtcXfOSDP4aUOrat0nJ9sirl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\np4jxJO84jlPE/AYJPHK+ZeQ5kAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2018a620da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## START CODE HERE ## (PUT YOUR IMAGE NAME) \n",
    "my_image = \"my_image.jpg\"   # change this to the name of your image file \n",
    "## END CODE HERE ##\n",
    "\n",
    "# We preprocess the image to fit your algorithm.\n",
    "fname = \"images/\" + my_image\n",
    "image = np.array(ndimage.imread(fname, flatten=False))\n",
    "my_image = scipy.misc.imresize(image, size=(num_px,num_px)).reshape((1, num_px*num_px*3)).T\n",
    "my_predicted_image = predict(d[\"w\"], d[\"b\"], my_image)\n",
    "\n",
    "plt.imshow(image)\n",
    "print(\"y = \" + str(np.squeeze(my_predicted_image)) + \", your algorithm predicts a \\\"\" + classes[int(np.squeeze(my_predicted_image)),].decode(\"utf-8\") +  \"\\\" picture.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What to remember from this assignment:**\n",
    "1. Preprocessing the dataset is important.\n",
    "2. You implemented each function separately: initialize(), propagate(), optimize(). Then you built a model().\n",
    "3. Tuning the learning rate (which is an example of a \"hyperparameter\") can make a big difference to the algorithm. You will see more examples of this later in this course!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, if you'd like, we invite you to try different things on this Notebook. Make sure you submit before trying anything. Once you submit, things you can play with include:\n",
    "    - Play with the learning rate and the number of iterations\n",
    "    - Try different initialization methods and compare the results\n",
    "    - Test other preprocessings (center the data, or divide each row by its standard deviation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bibliography:\n",
    "- http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/\n",
    "- https://stats.stackexchange.com/questions/211436/why-do-we-normalize-images-by-subtracting-the-datasets-image-mean-and-not-the-c"
   ]
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "neural-networks-deep-learning",
   "graded_item_id": "XaIWT",
   "launcher_item_id": "zAgPl"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
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